基因芯片数据的聚类功能评价算法和判别分析算法研究
|
摘要
以人类基因组计划(human genomes project, HGP)结束为标志,人类进入了后基因组时代。在后基因组时代,人类以研究基因功能为重点。基因芯片以其快速、高通量、准确性高等突出优点成为研究基因功能不可缺少的重要工具。基因芯片数据分析是基因芯片技术研究中的重要内容,属生物信息学研究领域。本文主要对基因芯片数据分析中的聚类功能评价和判别分析进行深入研究。
第一、聚类分析是基因芯片数据分析的重要方法,其目的是根据基因表达模式对基因分类,根据基因分类推测基因功能。然而由于聚类结果受到聚类算法和聚类参数的影响,使用不同的聚类算法和不同的聚类参数常常会产生不同的聚类结果,如何从基因功能相似性的角度评价聚类结果是聚类分析中的难点。本文第四章和第五章以此为切入点对聚类的功能评价算法进行研究。研究出了一种新的基因注释语义相似度计算方法,这种方法根据基因在基因本体(gene ontology, GO)上的注释计算基因的功能相似程度,并以酵母菌的异亮氨酸代谢通路和谷氨酸生物合成代谢通路为实验,证明了这种算法的准确性。在基因注释语义相似度计算方法的基础上,研究出了基因芯片数据聚类的功能评价算法,这种算法以类间基因功能的差异程度和类内的功能相似程度来评价聚类质量,并以酵母菌表达数据为例,表明用这种方法可以准确评价聚类结果的质量,在这种聚类功能评价算法的指导下可获得高质量的聚类结果。
第二、判别分析也是基因芯片数据分析的重要内容,是基因芯片应用于临床诊断必须解决的重要问题之一。我国是肝癌多发国家。microRNA芯片数据和基因芯片数据均可以对肝癌转移作出预测。microRNA通过调控相应靶基因的表达来发挥其生物功能。用来预测的microRNA和用来预测的基因,即特征microRNA和特征基因之间是否存在调控与被调控的关系?第六章以此为切入点对肝癌转移相关的特征microRNA和特征基因的提取,及两者间的关系进行了研究。研究出了一种t交叉权重的方法,这种方法以重复随机抽样进行t检验来计算基因的权重,t交叉权重的优点在于可以根据基因权重大小在判别分析中逐渐扩大特征基因集,与不同的支持向量机核函数结合,在交叉验证变化趋势的指导下,选择合适的特征microRNA集和特征基因集。结果在microRNA芯片数据集和基因芯片数据集中,分别选取了100个特征microRNA和710个特征基因。根据这100个microRNA的表达数据,用多项式核函数的支持向量机预测肝癌转移准确率在83.99%以上;根据这710个特征基因的表达数据,用线性核函数的支持向量机预测准确率在96.76%以上,表明预测准确度良好。对这些特征microRNA和特征基因作进一步分析,发现两者间存在调控与被调控的关系,这提示肝癌的转移可能与这些特征microRNA调控相应的特征基因有关。分析中还发现,特征基因集的功能主要富集于细胞周期代谢通路(P=0.0006),说明细胞周期代谢通路改变可能与肝癌转移有密切关系。
本文的创新点主要体现在以下几个方面:
(1)研究出了一种新的计算基因注释语义相似度算法。利用这种算法可以将基因功能相似性用数据形式度量出来,突破了以往只有模糊比较基因相似性的缺陷;利用这种算法可大批量比较基因的相似度,与手工相比具有高效准确等优点。
(2)研究出了一种新的基因表达数据聚类结果评价算法。该算法实现了从基因功能相似性的角度评价聚类结果,解决了以往只能从数据的数学特征评价聚类结果的不足,从而可获得更高质量的聚类结果。
(3)提出了一种新的特征基因提取方法。这种方法将多次t检验的结果转化为基因的权重值,根据权重值大小结合不同核函数的支持向量机来选择特征基因集和核函数,克服了随机试验选择特征基因集和核函数的缺点。
(4)发现了肝癌转移相关的特征microRNA与特征基因之间存在调控与被调控的关系。
对基因芯片数据的聚类功能评价算法研究和肝癌转移特征基因提取研究具有重要的学术价值和应用价值。首先利用聚类功能评价算法可获得更高质量的聚类结果,对基因功能作出更准确分类;其次提取的特征microRNA和特征基因可以提高预测肝癌转移的准确度;所构建的microRNAs-Genes调控网络为肝癌转移机理研究提供了新思路;同时基因注释语义相似度算法和t交叉权重法分别可用于其它类似的基因注释相似度比较和判别分析的研究中。
When the Human Genome Project (HGP) marked the end, humanity had entered a post-genome era, in which human focus on gene function research. Gene Chip, also called DNA microarray, with characteristics of fast, high-throughput, high accuracy, has become an important and indispensable tool for studies of gene function. Data analysis is an important aspect of gene chip technology. It belongs to the area of bioinformatics research. In the dissertation, mainly focus on two questions: the clustering functional evaluation and discriminant analysis to gene chip dataset.
The cluster analysis is an important approach in gene chip dataset analysis. The purpose of the analysis is to divide genes into groups based on gene expression patterns, and then to predict genes function using these groups. However, due to the clustering results are usually influenced by the clustering algorithm and/or its parameters. Clustering with different clustering algorithms or parameters often produces extremely diverse clustering results. How to evaluate these clustering results, especially from the perspective of biological function similarity, is a challenge in the cluster analysis of gene chip dataset. In Chapters IV and V, aiming directly at the question, study clustering functional evaluation algorithm. Develop a new approach to measure gene annotation semantic similarity. The algorithm based on Gene Ontology (GO) term locations measures the gene function similarity. Taken yeast metabolic pathway isoleucine and glutamic acid biosynthesis pathway as examples to show the accuracy of this algorithm. Based on the algorithm, raise a novel clustering functional evaluation to measure the quality of clustering results. This algorithm assesses the clustering quality using both differential degree between gene functions in separate clusters and similar degree between gene functions in the same cluster. Taking yeast expression data as an example, the results show that the method can accurately evaluate the quality of clustering results. Under the guidance of the evaluation approach, the higher-quality clustering results can be obtained.
The discriminant analysis to DNA microarray data is also an important content. It needs to be done for gene chip to be used in clinical diagnosis. China is a liver cancer-prone country. MicroRNA chip dataset and gene chip dataset all can be used to predict the metastasis of Hepatocellular carcinoma (HCC). The microRNA can regulate expression of corresponding target genes. Whether or not there is the regulation relationship between metastasis-related microRNAs (feature microRNAs) and genes (feature genes) in the HCC? Taking the problem as starting point in Chapter VI, study the identification of metastasis-related microRNAs and genes, and analyze their relationship. A novel approach, called t-cross-weight, was developed. The approach calculated weight for each gene through repeatly random sampling t-test. The advantage of t-cross-weight is that, according to rank of weight, can gradually broaden the set of feature microRNAs or feature genes, and use support vector machines (SVMs) with differential kernel function, under the guidance of k-cross-validation tendency to identify appropriate the set of feature microRNAs and of feature genes. The results suggest that 100 microRNAs and 710 genes were identified. According to the expression of the 100 feature microRNAs, employing the SVMs with polynomial kernel function, the accuracy rate of predicting metastasis of HCC is greater than 83.99%; and using linear kernel SVMs with the expression of the 710 feature genes, the accuracy rate is over 96.76%, which indicats significant prediction accuracy. Taking further analysis to these feature microRNAs and genes, found the existence of regulation relationship, which suggests that the metastasis of HCC may be associated with some feature microRNAs regulating some feature genes. Enrichment analysis to these feature genes with DAVID, an online tool, shows that the feature genes enriched in cell cycle pathway (p=0.0006), indicating the cell cycle pathway may be closely related to metastasis of HCC.
The innovations in this paper are mainly showed as follows:
1. Developed a new algorithm to measure similarity of gene annotation semantic. This algorithm measures similarity of gene function with the form of data, which breakthrough the defects of fuzzy in the previous gene function comparative. The similarities between a large numbers of genes can be easy obtained by using the algorithm, indicating that it is superior to the manual way in efficient and accurate.
2. Developed a novel clustering functional evaluation algorithm. The algorithm assesses the clustering results from the perspective of gene function similarity, so that it can overcome the previous drawback that clustering quality evaluation is only from the aspect of mathematical characteristics of data. Therefore, the result of higher quality can be obtained.
3. Proposed a new method to identify feature genes. This method transfers results of t-test into weight value. According to the weight values and SVMs of different kernel function to identify feature genes, which overcome the shortcomings that feature genes and kernel functions is selected by randomized trial ways.
4. Found a regulation relationship between feature microRNAs and genes in HCC metastasis.
The researches on the algorithm of clustering functional evaluation and the identification of metastasis-related genes and microRNAs in HCC have important academic and application value. Firstly, using the algorithm of clustering function evaluation can obtain higher-quality clustering results, which can divide genes into groups in more accurate functional classification. Secondly, these feature microRNAs and genes chose by the t-cross-weight can improve the prediction accuracy of HCC metastasis.Lasterly, the microRNAs-Genes associate network offers a new idea to the research on mechanism of metastasis of HCC. Otherwise, the algorithms of gene annotation semantic similarity and t-cross-weight also can be used to other similar gene functional compare and discriminant analysis, respectively.
第一、聚类分析是基因芯片数据分析的重要方法,其目的是根据基因表达模式对基因分类,根据基因分类推测基因功能。然而由于聚类结果受到聚类算法和聚类参数的影响,使用不同的聚类算法和不同的聚类参数常常会产生不同的聚类结果,如何从基因功能相似性的角度评价聚类结果是聚类分析中的难点。本文第四章和第五章以此为切入点对聚类的功能评价算法进行研究。研究出了一种新的基因注释语义相似度计算方法,这种方法根据基因在基因本体(gene ontology, GO)上的注释计算基因的功能相似程度,并以酵母菌的异亮氨酸代谢通路和谷氨酸生物合成代谢通路为实验,证明了这种算法的准确性。在基因注释语义相似度计算方法的基础上,研究出了基因芯片数据聚类的功能评价算法,这种算法以类间基因功能的差异程度和类内的功能相似程度来评价聚类质量,并以酵母菌表达数据为例,表明用这种方法可以准确评价聚类结果的质量,在这种聚类功能评价算法的指导下可获得高质量的聚类结果。
第二、判别分析也是基因芯片数据分析的重要内容,是基因芯片应用于临床诊断必须解决的重要问题之一。我国是肝癌多发国家。microRNA芯片数据和基因芯片数据均可以对肝癌转移作出预测。microRNA通过调控相应靶基因的表达来发挥其生物功能。用来预测的microRNA和用来预测的基因,即特征microRNA和特征基因之间是否存在调控与被调控的关系?第六章以此为切入点对肝癌转移相关的特征microRNA和特征基因的提取,及两者间的关系进行了研究。研究出了一种t交叉权重的方法,这种方法以重复随机抽样进行t检验来计算基因的权重,t交叉权重的优点在于可以根据基因权重大小在判别分析中逐渐扩大特征基因集,与不同的支持向量机核函数结合,在交叉验证变化趋势的指导下,选择合适的特征microRNA集和特征基因集。结果在microRNA芯片数据集和基因芯片数据集中,分别选取了100个特征microRNA和710个特征基因。根据这100个microRNA的表达数据,用多项式核函数的支持向量机预测肝癌转移准确率在83.99%以上;根据这710个特征基因的表达数据,用线性核函数的支持向量机预测准确率在96.76%以上,表明预测准确度良好。对这些特征microRNA和特征基因作进一步分析,发现两者间存在调控与被调控的关系,这提示肝癌的转移可能与这些特征microRNA调控相应的特征基因有关。分析中还发现,特征基因集的功能主要富集于细胞周期代谢通路(P=0.0006),说明细胞周期代谢通路改变可能与肝癌转移有密切关系。
本文的创新点主要体现在以下几个方面:
(1)研究出了一种新的计算基因注释语义相似度算法。利用这种算法可以将基因功能相似性用数据形式度量出来,突破了以往只有模糊比较基因相似性的缺陷;利用这种算法可大批量比较基因的相似度,与手工相比具有高效准确等优点。
(2)研究出了一种新的基因表达数据聚类结果评价算法。该算法实现了从基因功能相似性的角度评价聚类结果,解决了以往只能从数据的数学特征评价聚类结果的不足,从而可获得更高质量的聚类结果。
(3)提出了一种新的特征基因提取方法。这种方法将多次t检验的结果转化为基因的权重值,根据权重值大小结合不同核函数的支持向量机来选择特征基因集和核函数,克服了随机试验选择特征基因集和核函数的缺点。
(4)发现了肝癌转移相关的特征microRNA与特征基因之间存在调控与被调控的关系。
对基因芯片数据的聚类功能评价算法研究和肝癌转移特征基因提取研究具有重要的学术价值和应用价值。首先利用聚类功能评价算法可获得更高质量的聚类结果,对基因功能作出更准确分类;其次提取的特征microRNA和特征基因可以提高预测肝癌转移的准确度;所构建的microRNAs-Genes调控网络为肝癌转移机理研究提供了新思路;同时基因注释语义相似度算法和t交叉权重法分别可用于其它类似的基因注释相似度比较和判别分析的研究中。
When the Human Genome Project (HGP) marked the end, humanity had entered a post-genome era, in which human focus on gene function research. Gene Chip, also called DNA microarray, with characteristics of fast, high-throughput, high accuracy, has become an important and indispensable tool for studies of gene function. Data analysis is an important aspect of gene chip technology. It belongs to the area of bioinformatics research. In the dissertation, mainly focus on two questions: the clustering functional evaluation and discriminant analysis to gene chip dataset.
The cluster analysis is an important approach in gene chip dataset analysis. The purpose of the analysis is to divide genes into groups based on gene expression patterns, and then to predict genes function using these groups. However, due to the clustering results are usually influenced by the clustering algorithm and/or its parameters. Clustering with different clustering algorithms or parameters often produces extremely diverse clustering results. How to evaluate these clustering results, especially from the perspective of biological function similarity, is a challenge in the cluster analysis of gene chip dataset. In Chapters IV and V, aiming directly at the question, study clustering functional evaluation algorithm. Develop a new approach to measure gene annotation semantic similarity. The algorithm based on Gene Ontology (GO) term locations measures the gene function similarity. Taken yeast metabolic pathway isoleucine and glutamic acid biosynthesis pathway as examples to show the accuracy of this algorithm. Based on the algorithm, raise a novel clustering functional evaluation to measure the quality of clustering results. This algorithm assesses the clustering quality using both differential degree between gene functions in separate clusters and similar degree between gene functions in the same cluster. Taking yeast expression data as an example, the results show that the method can accurately evaluate the quality of clustering results. Under the guidance of the evaluation approach, the higher-quality clustering results can be obtained.
The discriminant analysis to DNA microarray data is also an important content. It needs to be done for gene chip to be used in clinical diagnosis. China is a liver cancer-prone country. MicroRNA chip dataset and gene chip dataset all can be used to predict the metastasis of Hepatocellular carcinoma (HCC). The microRNA can regulate expression of corresponding target genes. Whether or not there is the regulation relationship between metastasis-related microRNAs (feature microRNAs) and genes (feature genes) in the HCC? Taking the problem as starting point in Chapter VI, study the identification of metastasis-related microRNAs and genes, and analyze their relationship. A novel approach, called t-cross-weight, was developed. The approach calculated weight for each gene through repeatly random sampling t-test. The advantage of t-cross-weight is that, according to rank of weight, can gradually broaden the set of feature microRNAs or feature genes, and use support vector machines (SVMs) with differential kernel function, under the guidance of k-cross-validation tendency to identify appropriate the set of feature microRNAs and of feature genes. The results suggest that 100 microRNAs and 710 genes were identified. According to the expression of the 100 feature microRNAs, employing the SVMs with polynomial kernel function, the accuracy rate of predicting metastasis of HCC is greater than 83.99%; and using linear kernel SVMs with the expression of the 710 feature genes, the accuracy rate is over 96.76%, which indicats significant prediction accuracy. Taking further analysis to these feature microRNAs and genes, found the existence of regulation relationship, which suggests that the metastasis of HCC may be associated with some feature microRNAs regulating some feature genes. Enrichment analysis to these feature genes with DAVID, an online tool, shows that the feature genes enriched in cell cycle pathway (p=0.0006), indicating the cell cycle pathway may be closely related to metastasis of HCC.
The innovations in this paper are mainly showed as follows:
1. Developed a new algorithm to measure similarity of gene annotation semantic. This algorithm measures similarity of gene function with the form of data, which breakthrough the defects of fuzzy in the previous gene function comparative. The similarities between a large numbers of genes can be easy obtained by using the algorithm, indicating that it is superior to the manual way in efficient and accurate.
2. Developed a novel clustering functional evaluation algorithm. The algorithm assesses the clustering results from the perspective of gene function similarity, so that it can overcome the previous drawback that clustering quality evaluation is only from the aspect of mathematical characteristics of data. Therefore, the result of higher quality can be obtained.
3. Proposed a new method to identify feature genes. This method transfers results of t-test into weight value. According to the weight values and SVMs of different kernel function to identify feature genes, which overcome the shortcomings that feature genes and kernel functions is selected by randomized trial ways.
4. Found a regulation relationship between feature microRNAs and genes in HCC metastasis.
The researches on the algorithm of clustering functional evaluation and the identification of metastasis-related genes and microRNAs in HCC have important academic and application value. Firstly, using the algorithm of clustering function evaluation can obtain higher-quality clustering results, which can divide genes into groups in more accurate functional classification. Secondly, these feature microRNAs and genes chose by the t-cross-weight can improve the prediction accuracy of HCC metastasis.Lasterly, the microRNAs-Genes associate network offers a new idea to the research on mechanism of metastasis of HCC. Otherwise, the algorithms of gene annotation semantic similarity and t-cross-weight also can be used to other similar gene functional compare and discriminant analysis, respectively.
引文
[1]. Collins, F.S., M. Morgan, A. Patrinos. The Human Genome Project: lessons from large-scale biology [J]. Science, 2003. 300(5617): p. 286-90.
[2]. Roberts, L., R.J. Davenport, E. Pennisi, et al. A history of the Human Genome Project [J]. Science, 2001. 291(5507): p. 1195.
[3]. Sinha, S., P. Chattopadhyay. The Human Genome Project [J]. Natl Med J India, 2000. 13(4): p. 169-73.
[4]. Strausberg, R.L., E.A. Feingold, R.D. Klausner, et al. The mammalian gene collection [J]. Science, 1999. 286(5439): p. 455-7.
[5]. Gerhard, D.S., L. Wagner, E.A. Feingold, et al. The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC) [J]. Genome Res, 2004. 14(10B): p. 2121-7.
[6]. Avner, P., T. Bruls, I. Poras, et al. A radiation hybrid transcript map of the mouse genome [J]. Nat Genet, 2001. 29(2): p. 194-200.
[7]. The map-based sequence of the rice genome [J]. Nature, 2005. 436(7052): p. 793-800.
[8]. Sultan, M., M.H. Schulz, H. Richard, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome [J]. Science, 2008. 321(5891): p. 956-60.
[9]. Schuster, S.C. Next-generation sequencing transforms today's biology [J]. Nat Methods, 2008. 5(1): p. 16-8.
[10]. Reimers, M. Statistical analysis of microarray data [J]. Addict Biol, 2005. 10(1): p. 23-35.
[11]. Fodor, S.P., J.L. Read, M.C. Pirrung, et al. Light-directed, spatially addressable parallel chemical synthesis [J]. Science, 1991. 251(4995): p. 767-73.
[12]. Service, R.F. Microchip arrays put DNA on the spot [J]. Science, 1998. 282(5388): p. 396-9.
[13]. Beattie, W.G., L. Meng, S.L. Turner, et al. Hybridization of DNA targets to glass-tethered oligonucleotide probes [J]. Mol Biotechnol, 1995. 4(3): p. 213-25.
[14]. Gilles, P.N., D.J. Wu, C.B. Foster, et al. Single nucleotide polymorphic discrimination by an electronic dot blot assay on semiconductor microchips [J]. Nat Biotechnol, 1999. 17(4): p. 365-70.
[15]. Kerr, M.K., G.A. Churchill. Statistical design and the analysis of gene expression microarray data [J]. Genet Res, 2007. 89(5-6): p. 509-14.
[16]. Smyth, G.K., Y.H. Yang, T. Speed. Statistical issues in cDNA microarray data analysis [J]. Methods Mol Biol, 2003. 224: p. 111-36.
[17]. Taib, Z. Statistical analysis of oligonucleotide microarray data [J]. C R Biol, 2004. 327(3): p. 175-80.
[18]. Rensink, W.A., S.P. Hazen. Statistical issues in microarray data analysis [J]. Methods Mol Biol, 2006. 323: p. 359-66.
[19]. Fan, J., Y. Ren. Statistical analysis of DNA microarray data in cancer research [J]. Clin Cancer Res, 2006. 12(15): p. 4469-73.
[20]. Pirrung, M.C. Spatially Addressable Combinatorial Libraries [J]. Chem Rev, 1997. 97(2): p. 473-488.
[21]. Singh-Gasson, S., R.D. Green, Y. Yue, et al. Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array [J]. Nat Biotechnol, 1999. 17(10): p. 974-8.
[22].滕晓坤,肖华胜.基因芯片与高通量DNA测序技术前景分析[J].中国科学C辑:生命科学2008. 38(10): p. 891-899.
[23]. Schena, M., D. Shalon, R. Heller, et al. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes [J]. Proc Natl Acad Sci U S A, 1996. 93(20): p. 10614-9.
[24]. Shalon, D., S.J. Smith, P.O. Brown. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization [J]. Genome Res, 1996. 6(7): p. 639-45.
[25]. Schena, M., D. Shalon, R.W. Davis, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray [J]. Science, 1995. 270(5235): p. 467-70.
[26]. R, X.X., H. J, X.Z. G. Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver [J]. Proc Natl Acad Sci USA, 2001. 98(26): p. 5.
[27]. PF, X., H. NY, L. ZC. in situ synthesis of oligonucleotide arrays by using soft lighography [J]. nanotechnology, 2002. 13: p. 756-762.
[28].胡子有,马文丽,李凌等.大肠杆菌poly(A)化mRNA基因的芯片制备[J].微生物学通报, 2003(02).
[29].广东省防治非典型肺炎科技攻关病原分离专题组,吴清华,马文丽等.基因芯片中60mer寡核苷酸SARS冠状病毒探针的制备[J].广东医学, 2003(S1).
[30].吴清华,马文丽,王洪敏等.两种氨基修饰的基因芯片表面制备方法比较[J].第一军医大学学报, 2005(07).
[31].马晓冬,马文丽,吕梁等.炭疽杆菌保护性抗原基因芯片探针的制备[J].第一军医大学学报, 2003(08).
[32].黎志东,徐志凯. HIV亚型分析基因芯片的制备及应用[J].中国皮肤性病学杂志, 2003(03).
[33].孙朝晖,郑文岭,张宝等.乙型、丁型肝炎病毒诊断基因芯片制备的初步实验研究[J].肝脏, 2003(04).
[34].金慧英,陶开华,李越希等.检测霍乱弧菌的基因芯片的制备[J].中国公共卫生, 2004(04).
[35].孙朝晖,郑文岭,张宝等.基因芯片技术检测丁型肝炎病毒的研究[J].第一军医大学学报, 2004(01).
[36].何群, HBV基因芯片的制备及其在临床应用的研究[M].中国医科大学. 2005.
[37].张海燕,马文丽,石嵘等.黄热病病毒检测基因芯片的研究制备[J].广东医学, 2005(07).
[38]. Shiu, S.H., J.O. Borevitz. The next generation of microarray research: applications in evolutionary and ecological genomics [J]. Heredity, 2008. 100(2): p. 141-9.
[39]. Brazma, A. On the importance of standardisation in life sciences [J]. Bioinformatics, 2001. 17(2): p. 113-4.
[40]. Brazma, A., A. Robinson, G. Cameron, et al. One-stop shop for microarray data [J]. Nature, 2000. 403(6771): p. 699-700.
[41]. Knudsen, T.B., G.P. Daston. MIAME guidelines [J]. Reprod Toxicol, 2005. 19(3): p. 263.
[42]. Brazma, A., P. Hingamp, J. Quackenbush, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data [J]. Nat Genet, 2001. 29(4): p. 365-71.
[43]. del Rio, G., T.F. Bartley, H. del-Rio, et al. Mining DNA microarray data using a novel approach based on graph theory [J]. FEBS Lett, 2001. 509(2): p. 230-4.
[44]. Baldi, P., A.D. Long. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes [J]. Bioinformatics, 2001. 17(6): p. 509-19.
[45]. Kerr, M.K., G.A. Churchill. Statistical design and the analysis of gene expression microarray data [J]. Genet Res, 2001. 77(2): p. 123-8.
[46]. Draghici, S. Statistical intelligence: effective analysis of high-density microarray data [J]. Drug Discov Today, 2002. 7(11 Suppl): p. S55-63.
[47]. Ghosh, D., T.R. Barette, D. Rhodes, et al. Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer [J]. Funct Integr Genomics, 2003. 3(4): p. 180-8.
[48]. Gottardo, R., J.A. Pannucci, C.R. Kuske, et al. Statistical analysis of microarray data: a Bayesian approach [J]. Biostatistics, 2003. 4(4): p. 597-620.
[49]. Yang, D., S.O. Zakharkin, G.P. Page, et al. Applications of Bayesian statistical methods in microarray data analysis [J]. Am J Pharmacogenomics, 2004. 4(1): p. 53-62.
[50].吴斌,沈自尹.基因表达谱芯片的数据分析[J].世界华人消化杂志, 2006. 14(1): p. 68-74.
[51]. Gerhold, D., M. Lu, J. Xu, et al. Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays [J]. Physiol Genomics, 2001. 5(4): p. 161-70.
[52]. Mutch, D.M., A. Berger, R. Mansourian, et al. The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data [J]. BMC Bioinformatics, 2002. 3: p. 17.
[53]. Yang, I.V., E. Chen, J.P. Hasseman, et al. Within the fold: assessing differential expression measures and reproducibility in microarray assays [J]. Genome Biol, 2002. 3(11): p. research0062.
[54]. Black, M.A., R.W. Doerge. Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments [J]. Bioinformatics, 2002. 18(12): p. 1609-16.
[55]. Cui, X., G.A. Churchill. Statistical tests for differential expression in cDNA microarray experiments [J]. Genome Biol, 2003. 4(4): p. 210.
[56]. Raraty, M.G., J.A. Murphy, E. McLoughlin, et al. Mechanisms of acinar cell injury in acute pancreatitis [J]. Scand J Surg, 2005. 94(2): p. 89-96.
[57]. Fox, R.J., M.W. Dimmic. A two-sample Bayesian t-test for microarray data [J]. BMC Bioinformatics, 2006. 7: p. 126.
[58]. Mills, J.C., J.I. Gordon. A new approach for filtering noise from high-density oligonucleotide microarray datasets [J]. Nucleic Acids Res, 2001. 29(15): p. E72-2.
[59]. Aris, V.M., M.J. Cody, J. Cheng, et al. Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer [J]. BMC Bioinformatics, 2004. 5: p. 185.
[60]. Febbo, P.G., P.W. Kantoff. Noise and bias in microarray analysis of tumor specimens [J]. J Clin Oncol, 2006. 24(23): p. 3719-21.
[61]. Klebanov, L., A. Yakovlev. How high is the level of technical noise in microarray data? [J]. Biol Direct, 2007. 2: p. 9.
[62]. Troyanskaya, O.G., M.E. Garber, P.O. Brown, et al. Nonparametric methods for identifyingdifferentially expressed genes in microarray data [J]. Bioinformatics, 2002. 18(11): p. 1454-61.
[63]. Zhao, Y., W. Pan. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments [J]. Bioinformatics, 2003. 19(9): p. 1046-54.
[64]. Gao, X., P.X. Song. Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments [J]. BMC Bioinformatics, 2005. 6: p. 186.
[65]. Lee, M.L., G.A. Whitmore, H. Bjorkbacka, et al. Nonparametric methods for microarray data based on exchangeability and borrowed power [J]. J Biopharm Stat, 2005. 15(5): p. 783-97.
[66]. Zhang, S. An improved nonparametric approach for detecting differentially expressed genes with replicated microarray data [J]. Stat Appl Genet Mol Biol, 2006. 5: p. Article30.
[67]. Datta, S., G.A. Satten, D.J. Benos, et al. An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments [J]. Bioinformatics, 2004. 20(2): p. 235-42.
[68]. Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments [J]. Stat Appl Genet Mol Biol, 2004. 3: p. Article3.
[69]. Lin, D., Z. Shkedy, T. Burzykowski, et al. An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes [J]. Biom J, 2008. 50(5): p. 801-23.
[70]. Di Camillo, B., G. Toffolo, S.K. Nair, et al. Significance analysis of microarray transcript levels in time series experiments [J]. BMC Bioinformatics, 2007. 8 Suppl 1: p. S10.
[71]. Olson, N.E. The microarray data analysis process: from raw data to biological significance [J]. NeuroRx, 2006. 3(3): p. 373-83.
[72]. Storey, J.D., W. Xiao, J.T. Leek, et al. Significance analysis of time course microarray experiments [J]. Proc Natl Acad Sci U S A, 2005. 102(36): p. 12837-42.
[73]. Pavlidis, P. Using ANOVA for gene selection from microarray studies of the nervous system [J]. Methods, 2003. 31(4): p. 282-9.
[74]. Churchill, G.A. Using ANOVA to analyze microarray data [J]. Biotechniques, 2004. 37(2): p. 173-5, 177.
[75]. de Haan, J.R., R. Wehrens, S. Bauerschmidt, et al. Interpretation of ANOVA models for microarray data using PCA [J]. Bioinformatics, 2007. 23(2): p. 184-90.
[76]. Nueda, M.J., A. Conesa, J.A. Westerhuis, et al. Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA [J]. Bioinformatics, 2007. 23(14): p. 1792-800.
[77]. Aubert, J., A. Bar-Hen, J.J. Daudin, et al. Determination of the differentially expressed genes in microarray experiments using local FDR [J]. BMC Bioinformatics, 2004. 5: p. 125.
[78]. Assennato, G., P. Bruzzi. [Bonferroni in biomedical research] [J]. G Ital Nefrol, 2002. 19(2): p. 178-83.
[79]. Grant, G.R., J. Liu, C.J. Stoeckert, Jr. A practical false discovery rate approach to identifying patterns of differential expression in microarray data [J]. Bioinformatics, 2005. 21(11): p. 2684-90.
[80]. Pawitan, Y., S. Michiels, S. Koscielny, et al. False discovery rate, sensitivity and sample size for microarray studies [J]. Bioinformatics, 2005. 21(13): p. 3017-24.
[81]. Ploner, A., S. Calza, A. Gusnanto, et al. Multidimensional local false discovery rate for microarray studies [J]. Bioinformatics, 2006. 22(5): p. 556-65.
[82]. Cheng, C., S. Pounds. False discovery rate paradigms for statistical analyses of microarray geneexpression data [J]. Bioinformation, 2007. 1(10): p. 436-46.
[83]. Rhee, S.Y., V. Wood, K. Dolinski, et al. Use and misuse of the gene ontology annotations [J]. Nat Rev Genet, 2008. 9(7): p. 509-15.
[84]. Hong, E.L., R. Balakrishnan, Q. Dong, et al. Gene Ontology annotations at SGD: new data sources and annotation methods [J]. Nucleic Acids Res, 2008. 36(Database issue): p. D577-81.
[85]. Camon, E., D. Barrell, V. Lee, et al. The Gene Ontology Annotation (GOA) Database--an integrated resource of GO annotations to the UniProt Knowledgebase [J]. In Silico Biol, 2004. 4(1): p. 5-6.
[86]. Ogata, H., S. Goto, W. Fujibuchi, et al. Computation with the KEGG pathway database [J]. Biosystems, 1998. 47(1-2): p. 119-28.
[87]. Arakawa, K., N. Kono, Y. Yamada, et al. KEGG-based pathway visualization tool for complex omics data [J]. In Silico Biol, 2005. 5(4): p. 419-23.
[88]. Aoki-Kinoshita, K.F., M. Kanehisa. Gene annotation and pathway mapping in KEGG [J]. Methods Mol Biol, 2007. 396: p. 71-91.
[89]. Bickel, D.R. Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically [J]. Bioinformatics, 2003. 19(7): p. 818-24.
[90]. Azuaje, F. Clustering-based approaches to discovering and visualising microarray data patterns [J]. Brief Bioinform, 2003. 4(1): p. 31-42.
[91]. Michaels, G.S., D.B. Carr, M. Askenazi, et al. Cluster analysis and data visualization of large-scale gene expression data [J]. Pac Symp Biocomput, 1998: p. 42-53.
[92]. Guess, M.J., S.B. Wilson. Introduction to hierarchical clustering [J]. J Clin Neurophysiol, 2002. 19(2): p. 144-51.
[93]. Sullivan, L.M., K.A. Dukes, E. Losina. Tutorial in biostatistics. An introduction to hierarchical linear modelling [J]. Stat Med, 1999. 18(7): p. 855-88.
[94]. Levenstien, M.A., Y. Yang, J. Ott. Statistical significance for hierarchical clustering in genetic association and microarray expression studies [J]. BMC Bioinformatics, 2003. 4: p. 62.
[95]. Bertucci, F., S. Salas, S. Eysteries, et al. Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters [J]. Oncogene, 2004. 23(7): p. 1377-91.
[96]. Sorlie, T., C.M. Perou, R. Tibshirani, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications [J]. Proc Natl Acad Sci U S A, 2001. 98(19): p. 10869-74.
[97]. Steinley, D. Profiling local optima in K-means clustering: developing a diagnostic technique [J]. Psychol Methods, 2006. 11(2): p. 178-92.
[98]. Steinley, D. Local optima in K-means clustering: what you don't know may hurt you [J]. Psychol Methods, 2003. 8(3): p. 294-304.
[99]. Tsai, C.A., T.C. Lee, I.C. Ho, et al. Multi-class clustering and prediction in the analysis of microarray data [J]. Math Biosci, 2005. 193(1): p. 79-100.
[100]. Brock, A., S. Huang, D.E. Ingber. Identification of a distinct class of cytoskeleton-associated mRNAs using microarray technology [J]. BMC Cell Biol, 2003. 4: p. 6.
[101]. Furey, T.S., N. Cristianini, N. Duffy, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data [J]. Bioinformatics, 2000. 16(10): p. 906-14.
[102]. Pirooznia, M., Y. Deng. SVM Classifier - a comprehensive java interface for support vectormachine classification of microarray data [J]. BMC Bioinformatics, 2006. 7 Suppl 4: p. S25.
[103]. Sohn, I., S. Kim, C. Hwang, et al. Support vector machine quantile regression for detecting differentially expressed genes in microarray analysis [J]. Methods Inf Med, 2008. 47(5): p. 459-67.
[104]. Zhu, Y., X. Shen, W. Pan. Network-based support vector machine for classification of microarray samples [J]. BMC Bioinformatics, 2009. 10 Suppl 1: p. S21.
[105]. Williams, R.D., S.N. Hing, B.T. Greer, et al. Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines [J]. Genes Chromosomes Cancer, 2004. 41(1): p. 65-79.
[106]. Wang, L., J. Zhu, H. Zou. Hybrid huberized support vector machines for microarray classification and gene selection [J]. Bioinformatics, 2008. 24(3): p. 412-9.
[107]. Statnikov, A., L. Wang, C.F. Aliferis. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification [J]. BMC Bioinformatics, 2008. 9: p. 319.
[108]. Doran, M., D.S. Raicu, J.D. Furst, et al. Oligonucleotide microarray identification of Bacillus anthracis strains using support vector machines [J]. Bioinformatics, 2007. 23(4): p. 487-92.
[109]. Chu, F., L. Wang. Applications of support vector machines to cancer classification with microarray data [J]. Int J Neural Syst, 2005. 15(6): p. 475-84.
[110]. Peng, S., Q. Xu, X.B. Ling, et al. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines [J]. FEBS Lett, 2003. 555(2): p. 358-62.
[111]. Dangond, F., D. Hwang, S. Camelo, et al. Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter [J]. Physiol Genomics, 2004. 16(2): p. 229-39.
[112]. Cho, J.H., D. Lee, J.H. Park, et al. Gene selection and classification from microarray data using kernel machine [J]. FEBS Lett, 2004. 571(1-3): p. 93-8.
[113]. Billings, S.A., K.L. Lee. Nonlinear fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm [J]. Neural Netw, 2002. 15(2): p. 263-70.
[114]. Podgorelec, V., P. Kokol, B. Stiglic, et al. Decision trees: an overview and their use in medicine [J]. J Med Syst, 2002. 26(5): p. 445-63.
[115]. Dettling, M., P. Buhlmann. Boosting for tumor classification with gene expression data [J]. Bioinformatics, 2003. 19(9): p. 1061-9.
[116]. Middendorf, M., A. Kundaje, C. Wiggins, et al. Predicting genetic regulatory response using classification [J]. Bioinformatics, 2004. 20 Suppl 1: p. i232-40.
[117]. Agatonovic-Kustrin, S., R. Beresford. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research [J]. J Pharm Biomed Anal, 2000. 22(5): p. 717-27.
[118]. Lynn, K.S., W.L. Hsu, L.L. Li, et al. A Neural Network Model for Constructing Endophenotypes of Common Complex Diseases: -An Application to Male Young-onset Hypertension Microarray Data [J]. Bioinformatics, 2009.
[119]. O'Neill, M.C., L. Song. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect [J]. BMC Bioinformatics, 2003. 4: p. 13.
[120]. Sawa, T., L. Ohno-Machado. A neural network-based similarity index for clustering DNAmicroarray data [J]. Comput Biol Med, 2003. 33(1): p. 1-15.
[121]. Zhou, X., D.P. Tuck. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data [J]. Bioinformatics, 2007. 23(9): p. 1106-14.
[122]. Tang, Y., Y.Q. Zhang, Z. Huang. Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis [J]. IEEE/ACM Trans Comput Biol Bioinform, 2007. 4(3): p. 365-81.
[123]. Zhang, X., X. Lu, Q. Shi, et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data [J]. BMC Bioinformatics, 2006. 7: p. 197.
[124]. Phan, J., R. Moffitt, J. Dale, et al. Improvement of SVM Algorithm for Microarray Analysis Using Intelligent Parameter Selection [J]. Conf Proc IEEE Eng Med Biol Soc, 2005. 5: p. 4838-41.
[125]. Friedman, N., M. Linial, I. Nachman, et al. Using Bayesian networks to analyze expression data [J]. J Comput Biol, 2000. 7(3-4): p. 601-20.
[126]. Tamada, Y., S. Kim, H. Bannai, et al. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection [J]. Bioinformatics, 2003. 19 Suppl 2: p. ii227-36.
[127]. Kim, S., S. Imoto, S. Miyano. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data [J]. Biosystems, 2004. 75(1-3): p. 57-65.
[128]. Rogers, S., M. Girolami. A Bayesian regression approach to the inference of regulatory networks from gene expression data [J]. Bioinformatics, 2005. 21(14): p. 3131-7.
[129]. Smith, D.D., P. Saetrom, O. Snove, Jr., et al. Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation [J]. BMC Bioinformatics, 2008. 9: p. 63.
[130]. Kong, X., V. Mas, K.J. Archer. A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy [J]. BMC Genomics, 2008. 9: p. 98.
[131]. Hong, F., R. Breitling. A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments [J]. Bioinformatics, 2008. 24(3): p. 374-82.
[132]. Samal, B., M.J. Gerdin, D. Huddleston, et al. Meta-analysis of microarray-derived data from PACAP-deficient adrenal gland in vivo and PACAP-treated chromaffin cells identifies distinct classes of PACAP-regulated genes [J]. Peptides, 2007. 28(9): p. 1871-82.
[133]. Park, W.D., M.D. Stegall. A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds [J]. BMC Genomics, 2007. 8: p. 88.
[134]. Keegan, K.P., S. Pradhan, J.P. Wang, et al. Meta-analysis of Drosophila circadian microarray studies identifies a novel set of rhythmically expressed genes [J]. PLoS Comput Biol, 2007. 3(11): p. e208.
[135]. Jornsten, R., M. Ouyang, H.Y. Wang. A meta-data based method for DNA microarray imputation [J]. BMC Bioinformatics, 2007. 8: p. 109.
[136]. Cahan, P., F. Rovegno, D. Mooney, et al. Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization [J]. Gene, 2007. 401(1-2): p. 12-8.
[137]. Grutzmann, R., H. Boriss, O. Ammerpohl, et al. Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes [J]. Oncogene, 2005. 24(32): p. 5079-88.
[138]. Rhodes, D.R., J. Yu, K. Shanker, et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression [J]. Proc Natl Acad Sci U S A, 2004. 101(25): p. 9309-14.
[139]. Gunderson, K.L., F.J. Steemers, G. Lee, et al. A genome-wide scalable SNP genotyping assay using microarray technology [J]. Nat Genet, 2005. 37(5): p. 549-54.
[140]. Burton, P.R., D.G. Clayton, L.R. Cardon, et al. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants [J]. Nat Genet, 2007. 39(11): p. 1329-37.
[141]. Carter, N.P. Methods and strategies for analyzing copy number variation using DNA microarrays [J]. Nat Genet, 2007. 39(7 Suppl): p. S16-21.
[142]. Lee, C., A.J. Iafrate, A.R. Brothman. Copy number variations and clinical cytogenetic diagnosis of constitutional disorders [J]. Nat Genet, 2007. 39(7 Suppl): p. S48-54.
[143]. Manikandan, J., J.J. Aarthi, S.D. Kumar, et al. Oncomirs: The potential role of non-coding microRNAs in understanding cancer [J]. Bioinformation, 2008. 2(8): p. 330-4.
[144]. Grosshans, H., W. Filipowicz. Molecular biology: the expanding world of small RNAs [J]. Nature, 2008. 451(7177): p. 414-6.
[145]. Yin, J.Q., R.C. Zhao, K.V. Morris. Profiling microRNA expression with microarrays [J]. Trends Biotechnol, 2008. 26(2): p. 70-6.
[146]. Liu, C.G., G.A. Calin, S. Volinia, et al. MicroRNA expression profiling using microarrays [J]. Nat Protoc, 2008. 3(4): p. 563-78.
[147]. Barad, O., E. Meiri, A. Avniel, et al. MicroRNA expression detected by oligonucleotide microarrays: system establishment and expression profiling in human tissues [J]. Genome Res, 2004. 14(12): p. 2486-94.
[148]. Blenkiron, C., L.D. Goldstein, N.P. Thorne, et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype [J]. Genome Biol, 2007. 8(10): p. R214.
[149]. Yanaihara, N., N. Caplen, E. Bowman, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis [J]. Cancer Cell, 2006. 9(3): p. 189-98.
[150]. Mattie, M.D., C.C. Benz, J. Bowers, et al. Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies [J]. Mol Cancer, 2006. 5: p. 24.
[151]. Xiao, H.S., Q.H. Huang, F.X. Zhang, et al. Identification of gene expression profile of dorsal root ganglion in the rat peripheral axotomy model of neuropathic pain [J]. Proc Natl Acad Sci U S A, 2002. 99(12): p. 8360-5.
[152].范德刚,范清宇,张惠中等.利用基因芯片技术筛选不同转移能力骨肉瘤细胞差异表达基因[J].第四军医大学学报, 2003(09).
[153].王新颖,姜泊,赖卓胜等.基因芯片筛选大肠侧向发育型肿瘤相关基因的初步研究[J].第一军医大学学报, 2004(09).
[154].陈伟,付小兵,葛世丽等.寡核苷酸基因芯片筛选早期和晚期妊娠胎鼠皮肤内差异表达基因[J].中华医学杂志, 2005(07).
[155].郝希山,冯玉梅,张亮等.采用单引物扩增法标记的基因芯片技术筛选乳腺原发癌与淋巴结转移癌的差异表达基因[J].中华医学杂志, 2005(06).
[156].林嘉颖,杨学宁,杨矜记等.基因芯片筛选同时参与肺腺癌不同癌变进程的分子靶标[J].实用癌症杂志, 2005(03).
[157].宋波,唐建武,王波等.基因芯片筛选小鼠肝癌淋巴道转移相关基因[J].癌症, 2005(07).
[158].贺松琴,侯建国,谭晓洁等.应用基因芯片技术筛选前列腺癌转移相关基因[J].第二军医大学学报, 2006(11).
[159].雷平,杨树源,张建宁等.利用寡核苷酸基因芯片筛选人脑创伤的差异表达基因[J].中华实验外科杂志, 2006(12).
[160].石忠松,李明昌,孙毅明等.基因芯片筛选颅内动脉瘤炎性相关基因的研究[J].中华实验外科杂志, 2006(01).
[161].许沈华,余传定,牟瀚舟等.用基因芯片筛选胃黏膜癌变相关基因[J].中华胃肠外科杂志, 2006(05).
[162].张文晶,李红霞,宋磊等.应用基因芯片技术筛选卵巢癌紫杉醇耐药差异表达基因[J].肿瘤防治研究, 2006(01).
[163].郭遂群,邢福祺,庞战军等.基因芯片筛选的CFTR基因作为雌激素依赖型子宫内膜癌预后标志物的研究[J].广东医学, 2007(03).
[164].刘娟,姚树坤,殷飞.应用基因芯片筛选人肝癌、肝硬化及正常肝组织中差异表达的基因[J].基础医学与临床, 2007(01).
[165].周向东,钱桂生,刘凌志.采用基因芯片技术筛选人小细胞肺癌SH77/CDDP多药耐药相关基因表达谱的研究[J].第三军医大学学报, 2007(04).
[166].肖敏敏.基因芯片对白血病和淋巴瘤的分类和研究[J].白血病.淋巴瘤, 2002(02).
[167].黄庆,府伟灵,周玉等.基因芯片对人乳头瘤病毒的快速检测和分型[J].中华医院感染学杂志, 2005(04).
[168].雷平,杨树源,张建宁等.利用分类表达谱基因芯片筛选人脑创伤的差异表达基因[J].中华神经医学杂志, 2005(10).
[169].陈家林,邓超干,许晓清等.基因芯片对尖锐湿疣患者HPV的检测和基因分型[J].河北医学, 2006(02).
[170].赵敏,董汉生,陈雷等.尖锐湿疣组织HPV基因芯片分型及型别分析[J].中国皮肤性病学杂志, 2006(08).
[171].邹健,冉志华,萧树东.分类表达谱基因芯片在医学研究中的应用[J].肿瘤防治研究, 2006(03).
[172].荆志伟,王忠,王永炎等.基因芯片数据分析方法研究进展[J].生物技术通讯, 2007(01).
[173]. Bentley, D.R., S. Balasubramanian, H.P. Swerdlow, et al. Accurate whole human genome sequencing using reversible terminator chemistry [J]. Nature, 2008. 456(7218): p. 53-9.
[174]. Hillier, L.W., G.T. Marth, A.R. Quinlan, et al. Whole-genome sequencing and variant discovery in C. elegans [J]. Nat Methods, 2008. 5(2): p. 183-8.
[175]. Wang, J., W. Wang, R. Li, et al. The diploid genome sequence of an Asian individual [J]. Nature, 2008. 456(7218): p. 60-5.
[176]. Mortazavi, A., B.A. Williams, K. McCue, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq [J]. Nat Methods, 2008. 5(7): p. 621-8.
[177]. Sugarbaker, D.J., W.G. Richards, G.J. Gordon, et al. Transcriptome sequencing of malignant pleural mesothelioma tumors [J]. Proc Natl Acad Sci U S A, 2008. 105(9): p. 3521-6.
[178]. Zhao, T., G. Li, S. Mi, et al. A complex system of small RNAs in the unicellular green alga Chlamydomonas reinhardtii [J]. Genes Dev, 2007. 21(10): p. 1190-203.
[179]. Stark, A., P. Kheradpour, L. Parts, et al. Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes [J]. Genome Res, 2007. 17(12): p. 1865-79.
[180]. Barski, A., S. Cuddapah, K. Cui, et al. High-resolution profiling of histone methylations in thehuman genome [J]. Cell, 2007. 129(4): p. 823-37.
[181]. Grant, G.R., E. Manduchi, C.J. Stoeckert, Jr. Analysis and management of microarray gene expression data [J]. Curr Protoc Mol Biol, 2007. Chapter 19: p. Unit 19 6.
[182]. Stoeckert, C.J., Jr., H.C. Causton, C.A. Ball. Microarray databases: standards and ontologies [J]. Nat Genet, 2002. 32 Suppl: p. 469-73.
[183]. Cordero, F., M. Botta, R.A. Calogero. Microarray data analysis and mining approaches [J]. Brief Funct Genomic Proteomic, 2007. 6(4): p. 265-81.
[184].李瑶,基因芯片数据分析与处理[M]. 2006,北京:化学工业出版社.
[185]. Rousseeuw, P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis [J]. Journal of Computational Applications in Math, 1987(20): p. 53-65.
[186]. Ashburner, M., C.A. Ball, J.A. Blake, et al. Gene Ontology: tool for the unification of biology [J]. Nature Genetics, 2000. 25(1): p. 25-29.
[187]. Aoki, K.F., M. Kanehisa. Using the KEGG database resource [J]. Curr Protoc Bioinformatics, 2005. Chapter 1: p. Unit 1 12.
[188]. Kanehisa, M. The KEGG database [J]. Novartis Found Symp, 2002. 247: p. 91-101; discussion 101-3, 119-28, 244-52.
[189]. Kanehisa, M., M. Araki, S. Goto, et al. KEGG for linking genomes to life and the environment [J]. Nucleic Acids Res, 2008. 36(Database issue): p. D480-4.
[190]. Kanehisa, M., S. Goto. KEGG: kyoto encyclopedia of genes and genomes [J]. Nucleic Acids Res, 2000. 28(1): p. 27-30.
[191]. Kanehisa, M., S. Goto, M. Hattori, et al. From genomics to chemical genomics: new developments in KEGG [J]. Nucleic Acids Res, 2006. 34(Database issue): p. D354-7.
[192]. Kanehisa, M., S. Goto, S. Kawashima, et al. The KEGG databases at GenomeNet [J]. Nucleic Acids Res, 2002. 30(1): p. 42-6.
[193]. Kanehisa, M., S. Goto, S. Kawashima, et al. The KEGG resource for deciphering the genome [J]. Nucleic Acids Res, 2004. 32(Database issue): p. D277-80.
[194]. Barrett, T., D.B. Troup, S.E. Wilhite, et al. NCBI GEO: mining tens of millions of expression profiles--database and tools update [J]. Nucleic Acids Res, 2007. 35(Database issue): p. D760-5.
[195]. Edgar, R., T. Barrett. NCBI GEO standards and services for microarray data [J]. Nat Biotechnol, 2006. 24(12): p. 1471-2.
[196]. Saeed, A.I., N.K. Bhagabati, J.C. Braisted, et al. TM4 microarray software suite [J]. Methods Enzymol, 2006. 411: p. 134-93.
[197]. Saeed, A.I., V. Sharov, J. White, et al. TM4: a free, open-source system for microarray data management and analysis [J]. Biotechniques, 2003. 34(2): p. 374-8.
[198]. Durinck, S., Y. Moreau, A. Kasprzyk, et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis [J]. Bioinformatics, 2005. 21(16): p. 3439-40.
[199]. Zhao, Y., R. Simon. BRB-ArrayTools Data Archive for Human Cancer Gene Expression: A Unique and Efficient Data Sharing Resource [J]. Cancer Inform, 2008. 6: p. 9-15.
[200]. Huang da, W., B.T. Sherman, Q. Tan, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists [J]. Nucleic Acids Res, 2007. 35(Web Server issue): p. W169-75.
[201]. Schramm, A., J.H. Schulte, L. Klein-Hitpass, et al. Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling [J]. Oncogene, 2005. 24(53): p.7902-12.
[202]. Lee, Y., C.K. Lee. Classification of multiple cancer types by multicategory support vector machines using gene expression data [J]. Bioinformatics, 2003. 19(9): p. 1132-9.
[203]. Pavlidis, P., J. Weston, J. Cai, et al. Learning gene functional classifications from multiple data types [J]. J Comput Biol, 2002. 9(2): p. 401-11.
[204]. Man, M.Z., G. Dyson, K. Johnson, et al. Evaluating methods for classifying expression data [J]. J Biopharm Stat, 2004. 14(4): p. 1065-84.
[205]. Blazadonakis, M.E., M. Zervakis. Wrapper filtering criteria via linear neuron and kernel approaches [J]. Comput Biol Med, 2008. 38(8): p. 894-912.
[206]. Le Cao, K.A., O. Goncalves, P. Besse, et al. Selection of biologically relevant genes with a wrapper stochastic algorithm [J]. Stat Appl Genet Mol Biol, 2007. 6: p. Article29.
[207].张学工,统计学习理论的本质[M]. 1999,北京:清华大学出版社.
[208].许建华,张学工译,统计学习理论[M]. 2004,北京:电子工业出版社.
[209]. Harris, M.A., J. Clark, A. Ireland, et al. The Gene Ontology (GO) database and informatics resource [J]. Nucleic Acids Research, 2004. 32: p. D258-D261.
[210]. Resnik, P. Semantic similarity in a taxonomy: an information-based measure an its application to problems of ambiguity in natural language [J]. J. Artificial Intelligence Res., 1999(11): p. 95-130.
[211]. Sevilla, J.L., V. Segura, A. Podhorski, et al. Correlation between gene expression and GO semantic similarity [J]. Ieee-Acm Transactions on Computational Biology and Bioinformatiocs, 2005. 2(4): p. 330-338.
[212]. Jiang, J., D. Conrath, Semantic similarity Based on Corpus Statistics and Lexical Taxonomy, in tenth International Conference on Research on Computational Linguistics(ROCLING X). 1997: Taiwan.
[213]. Lin, D. An Information-Theoretic Definition of Similarity. in Proc. 15th International conference Machine Learing. 1998.
[214]. Couto, F.M., M.J. Silva, P.M. Coutinho. Measuring semantic similarity between Gene Ontology terms [J]. Data & Knowledge Engineering, 2007. 61(1): p. 137-152.
[215]. Lee, S.G., J.U. Hur, Y.S. Kim. A graph-theoretic modeling on GO space for biological interpretation of gene clusters [J]. Bioinformatics, 2004. 20(3): p. 381-388.
[216]. Jiang, J., D. Conrath. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. in tenth international Conference on Research on Computational Linguistics. 1997.
[217]. Wang, J.Z., Z.D. Du, R. Payattakool, et al. A new method to measure the semantic similarity of GO terms [J]. Bioinformatics, 2007. 23(10): p. 1274-1281.
[218]. Resnik, P., Using information Content to evaluate semantic similarity in taxonomy, in Proc. 14th Int'l Joint Conf. Artificial Intelligence. 1995. p. 448-453.
[219]. DeRisi, J.L., V.R. Iyer, P.O. Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale [J]. Science, 1997. 278(5338): p. 680-6.
[220]. Valentini, G. Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles [J]. Artif Intell Med, 2002. 26(3): p. 281-304.
[221]. Yu, S.L., H.Y. Chen, G.C. Chang, et al. MicroRNA signature predicts survival and relapse in lung cancer [J]. Cancer Cell, 2008. 13(1): p. 48-57.
[222]. Calin, G.A., M. Ferracin, A. Cimmino, et al. A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia [J]. N Engl J Med, 2005. 353(17): p. 1793-801.
[223]. Takamizawa, J., H. Konishi, K. Yanagisawa, et al. Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival [J]. Cancer Res, 2004. 64(11): p. 3753-6.
[224]. Iorio, M.V., M. Ferracin, C.G. Liu, et al. MicroRNA gene expression deregulation in human breast cancer [J]. Cancer Res, 2005. 65(16): p. 7065-70.
[225]. Bloomston, M., W.L. Frankel, F. Petrocca, et al. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis [J]. JAMA, 2007. 297(17): p. 1901-8.
[226]. Parkin, D.M., F. Bray, J. Ferlay, et al. Global cancer statistics, 2002 [J]. CA Cancer J Clin, 2005. 55(2): p. 74-108.
[227]. Parkin, D.M. Global cancer statistics in the year 2000 [J]. Lancet Oncol, 2001. 2(9): p. 533-43.
[228]. Ke, A.W., G.M. Shi, J. Zhou, et al. Role of overexpression of CD151 and/or c-Met in predicting prognosis of hepatocellular carcinoma [J]. HEPATOLOGY, 2009. 49(2): p. 491-503.
[229]. Ye, Q.H., L.X. Qin, M. Forgues, et al. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning [J]. Nat Med, 2003. 9(4): p. 416-23.
[230]. Budhu, A., H.L. Jia, M. Forgues, et al. Identification of metastasis-related microRNAs in hepatocellular carcinoma [J]. HEPATOLOGY, 2008. 47(3): p. 897-907.
[231]. Zhao, A., K. Dou, K. Li, et al. [The effects of the expression of VEGF and KDR on the angiogenesis, growth and metastasis of hepatocellular carcinoma] [J]. Zhonghua Wai Ke Za Zhi, 2000. 38(6): p. 453-6.
[232]. Iguchi, H., M. Yokota, M. Fukutomi, et al. A possible role of VEGF in osteolytic bone metastasis of hepatocellular carcinoma [J]. J Exp Clin Cancer Res, 2002. 21(3): p. 309-13.
[233]. Meng, C., X. Chen. [Association of VEGF, uPA, ICAM-1 and PCNA expression with metastasis and recurrence in hepato cellular carcinoma] [J]. Zhonghua Wai Ke Za Zhi, 2002. 40(9): p. 673-5.
[234]. Hirohashi, K., T. Yamamoto, T. Uenishi, et al. CD44 and VEGF expression in extrahepatic metastasis of human hepatocellular carcinoma [J]. Hepatogastroenterology, 2004. 51(58): p. 1121-3.
[235]. Tsai, Y.C., A. Mendoza, J.M. Mariano, et al. The ubiquitin ligase gp78 promotes sarcoma metastasis by targeting KAI1 for degradation [J]. Nat Med, 2007. 13(12): p. 1504-9.
[236]. Qi, J., K. Nakayama, S. Gaitonde, et al. The ubiquitin ligase Siah2 regulates tumorigenesis and metastasis by HIF-dependent and -independent pathways [J]. Proc Natl Acad Sci U S A, 2008. 105(43): p. 16713-8.
[2]. Roberts, L., R.J. Davenport, E. Pennisi, et al. A history of the Human Genome Project [J]. Science, 2001. 291(5507): p. 1195.
[3]. Sinha, S., P. Chattopadhyay. The Human Genome Project [J]. Natl Med J India, 2000. 13(4): p. 169-73.
[4]. Strausberg, R.L., E.A. Feingold, R.D. Klausner, et al. The mammalian gene collection [J]. Science, 1999. 286(5439): p. 455-7.
[5]. Gerhard, D.S., L. Wagner, E.A. Feingold, et al. The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC) [J]. Genome Res, 2004. 14(10B): p. 2121-7.
[6]. Avner, P., T. Bruls, I. Poras, et al. A radiation hybrid transcript map of the mouse genome [J]. Nat Genet, 2001. 29(2): p. 194-200.
[7]. The map-based sequence of the rice genome [J]. Nature, 2005. 436(7052): p. 793-800.
[8]. Sultan, M., M.H. Schulz, H. Richard, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome [J]. Science, 2008. 321(5891): p. 956-60.
[9]. Schuster, S.C. Next-generation sequencing transforms today's biology [J]. Nat Methods, 2008. 5(1): p. 16-8.
[10]. Reimers, M. Statistical analysis of microarray data [J]. Addict Biol, 2005. 10(1): p. 23-35.
[11]. Fodor, S.P., J.L. Read, M.C. Pirrung, et al. Light-directed, spatially addressable parallel chemical synthesis [J]. Science, 1991. 251(4995): p. 767-73.
[12]. Service, R.F. Microchip arrays put DNA on the spot [J]. Science, 1998. 282(5388): p. 396-9.
[13]. Beattie, W.G., L. Meng, S.L. Turner, et al. Hybridization of DNA targets to glass-tethered oligonucleotide probes [J]. Mol Biotechnol, 1995. 4(3): p. 213-25.
[14]. Gilles, P.N., D.J. Wu, C.B. Foster, et al. Single nucleotide polymorphic discrimination by an electronic dot blot assay on semiconductor microchips [J]. Nat Biotechnol, 1999. 17(4): p. 365-70.
[15]. Kerr, M.K., G.A. Churchill. Statistical design and the analysis of gene expression microarray data [J]. Genet Res, 2007. 89(5-6): p. 509-14.
[16]. Smyth, G.K., Y.H. Yang, T. Speed. Statistical issues in cDNA microarray data analysis [J]. Methods Mol Biol, 2003. 224: p. 111-36.
[17]. Taib, Z. Statistical analysis of oligonucleotide microarray data [J]. C R Biol, 2004. 327(3): p. 175-80.
[18]. Rensink, W.A., S.P. Hazen. Statistical issues in microarray data analysis [J]. Methods Mol Biol, 2006. 323: p. 359-66.
[19]. Fan, J., Y. Ren. Statistical analysis of DNA microarray data in cancer research [J]. Clin Cancer Res, 2006. 12(15): p. 4469-73.
[20]. Pirrung, M.C. Spatially Addressable Combinatorial Libraries [J]. Chem Rev, 1997. 97(2): p. 473-488.
[21]. Singh-Gasson, S., R.D. Green, Y. Yue, et al. Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array [J]. Nat Biotechnol, 1999. 17(10): p. 974-8.
[22].滕晓坤,肖华胜.基因芯片与高通量DNA测序技术前景分析[J].中国科学C辑:生命科学2008. 38(10): p. 891-899.
[23]. Schena, M., D. Shalon, R. Heller, et al. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes [J]. Proc Natl Acad Sci U S A, 1996. 93(20): p. 10614-9.
[24]. Shalon, D., S.J. Smith, P.O. Brown. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization [J]. Genome Res, 1996. 6(7): p. 639-45.
[25]. Schena, M., D. Shalon, R.W. Davis, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray [J]. Science, 1995. 270(5235): p. 467-70.
[26]. R, X.X., H. J, X.Z. G. Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver [J]. Proc Natl Acad Sci USA, 2001. 98(26): p. 5.
[27]. PF, X., H. NY, L. ZC. in situ synthesis of oligonucleotide arrays by using soft lighography [J]. nanotechnology, 2002. 13: p. 756-762.
[28].胡子有,马文丽,李凌等.大肠杆菌poly(A)化mRNA基因的芯片制备[J].微生物学通报, 2003(02).
[29].广东省防治非典型肺炎科技攻关病原分离专题组,吴清华,马文丽等.基因芯片中60mer寡核苷酸SARS冠状病毒探针的制备[J].广东医学, 2003(S1).
[30].吴清华,马文丽,王洪敏等.两种氨基修饰的基因芯片表面制备方法比较[J].第一军医大学学报, 2005(07).
[31].马晓冬,马文丽,吕梁等.炭疽杆菌保护性抗原基因芯片探针的制备[J].第一军医大学学报, 2003(08).
[32].黎志东,徐志凯. HIV亚型分析基因芯片的制备及应用[J].中国皮肤性病学杂志, 2003(03).
[33].孙朝晖,郑文岭,张宝等.乙型、丁型肝炎病毒诊断基因芯片制备的初步实验研究[J].肝脏, 2003(04).
[34].金慧英,陶开华,李越希等.检测霍乱弧菌的基因芯片的制备[J].中国公共卫生, 2004(04).
[35].孙朝晖,郑文岭,张宝等.基因芯片技术检测丁型肝炎病毒的研究[J].第一军医大学学报, 2004(01).
[36].何群, HBV基因芯片的制备及其在临床应用的研究[M].中国医科大学. 2005.
[37].张海燕,马文丽,石嵘等.黄热病病毒检测基因芯片的研究制备[J].广东医学, 2005(07).
[38]. Shiu, S.H., J.O. Borevitz. The next generation of microarray research: applications in evolutionary and ecological genomics [J]. Heredity, 2008. 100(2): p. 141-9.
[39]. Brazma, A. On the importance of standardisation in life sciences [J]. Bioinformatics, 2001. 17(2): p. 113-4.
[40]. Brazma, A., A. Robinson, G. Cameron, et al. One-stop shop for microarray data [J]. Nature, 2000. 403(6771): p. 699-700.
[41]. Knudsen, T.B., G.P. Daston. MIAME guidelines [J]. Reprod Toxicol, 2005. 19(3): p. 263.
[42]. Brazma, A., P. Hingamp, J. Quackenbush, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data [J]. Nat Genet, 2001. 29(4): p. 365-71.
[43]. del Rio, G., T.F. Bartley, H. del-Rio, et al. Mining DNA microarray data using a novel approach based on graph theory [J]. FEBS Lett, 2001. 509(2): p. 230-4.
[44]. Baldi, P., A.D. Long. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes [J]. Bioinformatics, 2001. 17(6): p. 509-19.
[45]. Kerr, M.K., G.A. Churchill. Statistical design and the analysis of gene expression microarray data [J]. Genet Res, 2001. 77(2): p. 123-8.
[46]. Draghici, S. Statistical intelligence: effective analysis of high-density microarray data [J]. Drug Discov Today, 2002. 7(11 Suppl): p. S55-63.
[47]. Ghosh, D., T.R. Barette, D. Rhodes, et al. Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer [J]. Funct Integr Genomics, 2003. 3(4): p. 180-8.
[48]. Gottardo, R., J.A. Pannucci, C.R. Kuske, et al. Statistical analysis of microarray data: a Bayesian approach [J]. Biostatistics, 2003. 4(4): p. 597-620.
[49]. Yang, D., S.O. Zakharkin, G.P. Page, et al. Applications of Bayesian statistical methods in microarray data analysis [J]. Am J Pharmacogenomics, 2004. 4(1): p. 53-62.
[50].吴斌,沈自尹.基因表达谱芯片的数据分析[J].世界华人消化杂志, 2006. 14(1): p. 68-74.
[51]. Gerhold, D., M. Lu, J. Xu, et al. Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays [J]. Physiol Genomics, 2001. 5(4): p. 161-70.
[52]. Mutch, D.M., A. Berger, R. Mansourian, et al. The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data [J]. BMC Bioinformatics, 2002. 3: p. 17.
[53]. Yang, I.V., E. Chen, J.P. Hasseman, et al. Within the fold: assessing differential expression measures and reproducibility in microarray assays [J]. Genome Biol, 2002. 3(11): p. research0062.
[54]. Black, M.A., R.W. Doerge. Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments [J]. Bioinformatics, 2002. 18(12): p. 1609-16.
[55]. Cui, X., G.A. Churchill. Statistical tests for differential expression in cDNA microarray experiments [J]. Genome Biol, 2003. 4(4): p. 210.
[56]. Raraty, M.G., J.A. Murphy, E. McLoughlin, et al. Mechanisms of acinar cell injury in acute pancreatitis [J]. Scand J Surg, 2005. 94(2): p. 89-96.
[57]. Fox, R.J., M.W. Dimmic. A two-sample Bayesian t-test for microarray data [J]. BMC Bioinformatics, 2006. 7: p. 126.
[58]. Mills, J.C., J.I. Gordon. A new approach for filtering noise from high-density oligonucleotide microarray datasets [J]. Nucleic Acids Res, 2001. 29(15): p. E72-2.
[59]. Aris, V.M., M.J. Cody, J. Cheng, et al. Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer [J]. BMC Bioinformatics, 2004. 5: p. 185.
[60]. Febbo, P.G., P.W. Kantoff. Noise and bias in microarray analysis of tumor specimens [J]. J Clin Oncol, 2006. 24(23): p. 3719-21.
[61]. Klebanov, L., A. Yakovlev. How high is the level of technical noise in microarray data? [J]. Biol Direct, 2007. 2: p. 9.
[62]. Troyanskaya, O.G., M.E. Garber, P.O. Brown, et al. Nonparametric methods for identifyingdifferentially expressed genes in microarray data [J]. Bioinformatics, 2002. 18(11): p. 1454-61.
[63]. Zhao, Y., W. Pan. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments [J]. Bioinformatics, 2003. 19(9): p. 1046-54.
[64]. Gao, X., P.X. Song. Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments [J]. BMC Bioinformatics, 2005. 6: p. 186.
[65]. Lee, M.L., G.A. Whitmore, H. Bjorkbacka, et al. Nonparametric methods for microarray data based on exchangeability and borrowed power [J]. J Biopharm Stat, 2005. 15(5): p. 783-97.
[66]. Zhang, S. An improved nonparametric approach for detecting differentially expressed genes with replicated microarray data [J]. Stat Appl Genet Mol Biol, 2006. 5: p. Article30.
[67]. Datta, S., G.A. Satten, D.J. Benos, et al. An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments [J]. Bioinformatics, 2004. 20(2): p. 235-42.
[68]. Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments [J]. Stat Appl Genet Mol Biol, 2004. 3: p. Article3.
[69]. Lin, D., Z. Shkedy, T. Burzykowski, et al. An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes [J]. Biom J, 2008. 50(5): p. 801-23.
[70]. Di Camillo, B., G. Toffolo, S.K. Nair, et al. Significance analysis of microarray transcript levels in time series experiments [J]. BMC Bioinformatics, 2007. 8 Suppl 1: p. S10.
[71]. Olson, N.E. The microarray data analysis process: from raw data to biological significance [J]. NeuroRx, 2006. 3(3): p. 373-83.
[72]. Storey, J.D., W. Xiao, J.T. Leek, et al. Significance analysis of time course microarray experiments [J]. Proc Natl Acad Sci U S A, 2005. 102(36): p. 12837-42.
[73]. Pavlidis, P. Using ANOVA for gene selection from microarray studies of the nervous system [J]. Methods, 2003. 31(4): p. 282-9.
[74]. Churchill, G.A. Using ANOVA to analyze microarray data [J]. Biotechniques, 2004. 37(2): p. 173-5, 177.
[75]. de Haan, J.R., R. Wehrens, S. Bauerschmidt, et al. Interpretation of ANOVA models for microarray data using PCA [J]. Bioinformatics, 2007. 23(2): p. 184-90.
[76]. Nueda, M.J., A. Conesa, J.A. Westerhuis, et al. Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA [J]. Bioinformatics, 2007. 23(14): p. 1792-800.
[77]. Aubert, J., A. Bar-Hen, J.J. Daudin, et al. Determination of the differentially expressed genes in microarray experiments using local FDR [J]. BMC Bioinformatics, 2004. 5: p. 125.
[78]. Assennato, G., P. Bruzzi. [Bonferroni in biomedical research] [J]. G Ital Nefrol, 2002. 19(2): p. 178-83.
[79]. Grant, G.R., J. Liu, C.J. Stoeckert, Jr. A practical false discovery rate approach to identifying patterns of differential expression in microarray data [J]. Bioinformatics, 2005. 21(11): p. 2684-90.
[80]. Pawitan, Y., S. Michiels, S. Koscielny, et al. False discovery rate, sensitivity and sample size for microarray studies [J]. Bioinformatics, 2005. 21(13): p. 3017-24.
[81]. Ploner, A., S. Calza, A. Gusnanto, et al. Multidimensional local false discovery rate for microarray studies [J]. Bioinformatics, 2006. 22(5): p. 556-65.
[82]. Cheng, C., S. Pounds. False discovery rate paradigms for statistical analyses of microarray geneexpression data [J]. Bioinformation, 2007. 1(10): p. 436-46.
[83]. Rhee, S.Y., V. Wood, K. Dolinski, et al. Use and misuse of the gene ontology annotations [J]. Nat Rev Genet, 2008. 9(7): p. 509-15.
[84]. Hong, E.L., R. Balakrishnan, Q. Dong, et al. Gene Ontology annotations at SGD: new data sources and annotation methods [J]. Nucleic Acids Res, 2008. 36(Database issue): p. D577-81.
[85]. Camon, E., D. Barrell, V. Lee, et al. The Gene Ontology Annotation (GOA) Database--an integrated resource of GO annotations to the UniProt Knowledgebase [J]. In Silico Biol, 2004. 4(1): p. 5-6.
[86]. Ogata, H., S. Goto, W. Fujibuchi, et al. Computation with the KEGG pathway database [J]. Biosystems, 1998. 47(1-2): p. 119-28.
[87]. Arakawa, K., N. Kono, Y. Yamada, et al. KEGG-based pathway visualization tool for complex omics data [J]. In Silico Biol, 2005. 5(4): p. 419-23.
[88]. Aoki-Kinoshita, K.F., M. Kanehisa. Gene annotation and pathway mapping in KEGG [J]. Methods Mol Biol, 2007. 396: p. 71-91.
[89]. Bickel, D.R. Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically [J]. Bioinformatics, 2003. 19(7): p. 818-24.
[90]. Azuaje, F. Clustering-based approaches to discovering and visualising microarray data patterns [J]. Brief Bioinform, 2003. 4(1): p. 31-42.
[91]. Michaels, G.S., D.B. Carr, M. Askenazi, et al. Cluster analysis and data visualization of large-scale gene expression data [J]. Pac Symp Biocomput, 1998: p. 42-53.
[92]. Guess, M.J., S.B. Wilson. Introduction to hierarchical clustering [J]. J Clin Neurophysiol, 2002. 19(2): p. 144-51.
[93]. Sullivan, L.M., K.A. Dukes, E. Losina. Tutorial in biostatistics. An introduction to hierarchical linear modelling [J]. Stat Med, 1999. 18(7): p. 855-88.
[94]. Levenstien, M.A., Y. Yang, J. Ott. Statistical significance for hierarchical clustering in genetic association and microarray expression studies [J]. BMC Bioinformatics, 2003. 4: p. 62.
[95]. Bertucci, F., S. Salas, S. Eysteries, et al. Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters [J]. Oncogene, 2004. 23(7): p. 1377-91.
[96]. Sorlie, T., C.M. Perou, R. Tibshirani, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications [J]. Proc Natl Acad Sci U S A, 2001. 98(19): p. 10869-74.
[97]. Steinley, D. Profiling local optima in K-means clustering: developing a diagnostic technique [J]. Psychol Methods, 2006. 11(2): p. 178-92.
[98]. Steinley, D. Local optima in K-means clustering: what you don't know may hurt you [J]. Psychol Methods, 2003. 8(3): p. 294-304.
[99]. Tsai, C.A., T.C. Lee, I.C. Ho, et al. Multi-class clustering and prediction in the analysis of microarray data [J]. Math Biosci, 2005. 193(1): p. 79-100.
[100]. Brock, A., S. Huang, D.E. Ingber. Identification of a distinct class of cytoskeleton-associated mRNAs using microarray technology [J]. BMC Cell Biol, 2003. 4: p. 6.
[101]. Furey, T.S., N. Cristianini, N. Duffy, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data [J]. Bioinformatics, 2000. 16(10): p. 906-14.
[102]. Pirooznia, M., Y. Deng. SVM Classifier - a comprehensive java interface for support vectormachine classification of microarray data [J]. BMC Bioinformatics, 2006. 7 Suppl 4: p. S25.
[103]. Sohn, I., S. Kim, C. Hwang, et al. Support vector machine quantile regression for detecting differentially expressed genes in microarray analysis [J]. Methods Inf Med, 2008. 47(5): p. 459-67.
[104]. Zhu, Y., X. Shen, W. Pan. Network-based support vector machine for classification of microarray samples [J]. BMC Bioinformatics, 2009. 10 Suppl 1: p. S21.
[105]. Williams, R.D., S.N. Hing, B.T. Greer, et al. Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines [J]. Genes Chromosomes Cancer, 2004. 41(1): p. 65-79.
[106]. Wang, L., J. Zhu, H. Zou. Hybrid huberized support vector machines for microarray classification and gene selection [J]. Bioinformatics, 2008. 24(3): p. 412-9.
[107]. Statnikov, A., L. Wang, C.F. Aliferis. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification [J]. BMC Bioinformatics, 2008. 9: p. 319.
[108]. Doran, M., D.S. Raicu, J.D. Furst, et al. Oligonucleotide microarray identification of Bacillus anthracis strains using support vector machines [J]. Bioinformatics, 2007. 23(4): p. 487-92.
[109]. Chu, F., L. Wang. Applications of support vector machines to cancer classification with microarray data [J]. Int J Neural Syst, 2005. 15(6): p. 475-84.
[110]. Peng, S., Q. Xu, X.B. Ling, et al. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines [J]. FEBS Lett, 2003. 555(2): p. 358-62.
[111]. Dangond, F., D. Hwang, S. Camelo, et al. Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter [J]. Physiol Genomics, 2004. 16(2): p. 229-39.
[112]. Cho, J.H., D. Lee, J.H. Park, et al. Gene selection and classification from microarray data using kernel machine [J]. FEBS Lett, 2004. 571(1-3): p. 93-8.
[113]. Billings, S.A., K.L. Lee. Nonlinear fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm [J]. Neural Netw, 2002. 15(2): p. 263-70.
[114]. Podgorelec, V., P. Kokol, B. Stiglic, et al. Decision trees: an overview and their use in medicine [J]. J Med Syst, 2002. 26(5): p. 445-63.
[115]. Dettling, M., P. Buhlmann. Boosting for tumor classification with gene expression data [J]. Bioinformatics, 2003. 19(9): p. 1061-9.
[116]. Middendorf, M., A. Kundaje, C. Wiggins, et al. Predicting genetic regulatory response using classification [J]. Bioinformatics, 2004. 20 Suppl 1: p. i232-40.
[117]. Agatonovic-Kustrin, S., R. Beresford. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research [J]. J Pharm Biomed Anal, 2000. 22(5): p. 717-27.
[118]. Lynn, K.S., W.L. Hsu, L.L. Li, et al. A Neural Network Model for Constructing Endophenotypes of Common Complex Diseases: -An Application to Male Young-onset Hypertension Microarray Data [J]. Bioinformatics, 2009.
[119]. O'Neill, M.C., L. Song. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect [J]. BMC Bioinformatics, 2003. 4: p. 13.
[120]. Sawa, T., L. Ohno-Machado. A neural network-based similarity index for clustering DNAmicroarray data [J]. Comput Biol Med, 2003. 33(1): p. 1-15.
[121]. Zhou, X., D.P. Tuck. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data [J]. Bioinformatics, 2007. 23(9): p. 1106-14.
[122]. Tang, Y., Y.Q. Zhang, Z. Huang. Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis [J]. IEEE/ACM Trans Comput Biol Bioinform, 2007. 4(3): p. 365-81.
[123]. Zhang, X., X. Lu, Q. Shi, et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data [J]. BMC Bioinformatics, 2006. 7: p. 197.
[124]. Phan, J., R. Moffitt, J. Dale, et al. Improvement of SVM Algorithm for Microarray Analysis Using Intelligent Parameter Selection [J]. Conf Proc IEEE Eng Med Biol Soc, 2005. 5: p. 4838-41.
[125]. Friedman, N., M. Linial, I. Nachman, et al. Using Bayesian networks to analyze expression data [J]. J Comput Biol, 2000. 7(3-4): p. 601-20.
[126]. Tamada, Y., S. Kim, H. Bannai, et al. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection [J]. Bioinformatics, 2003. 19 Suppl 2: p. ii227-36.
[127]. Kim, S., S. Imoto, S. Miyano. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data [J]. Biosystems, 2004. 75(1-3): p. 57-65.
[128]. Rogers, S., M. Girolami. A Bayesian regression approach to the inference of regulatory networks from gene expression data [J]. Bioinformatics, 2005. 21(14): p. 3131-7.
[129]. Smith, D.D., P. Saetrom, O. Snove, Jr., et al. Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation [J]. BMC Bioinformatics, 2008. 9: p. 63.
[130]. Kong, X., V. Mas, K.J. Archer. A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy [J]. BMC Genomics, 2008. 9: p. 98.
[131]. Hong, F., R. Breitling. A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments [J]. Bioinformatics, 2008. 24(3): p. 374-82.
[132]. Samal, B., M.J. Gerdin, D. Huddleston, et al. Meta-analysis of microarray-derived data from PACAP-deficient adrenal gland in vivo and PACAP-treated chromaffin cells identifies distinct classes of PACAP-regulated genes [J]. Peptides, 2007. 28(9): p. 1871-82.
[133]. Park, W.D., M.D. Stegall. A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds [J]. BMC Genomics, 2007. 8: p. 88.
[134]. Keegan, K.P., S. Pradhan, J.P. Wang, et al. Meta-analysis of Drosophila circadian microarray studies identifies a novel set of rhythmically expressed genes [J]. PLoS Comput Biol, 2007. 3(11): p. e208.
[135]. Jornsten, R., M. Ouyang, H.Y. Wang. A meta-data based method for DNA microarray imputation [J]. BMC Bioinformatics, 2007. 8: p. 109.
[136]. Cahan, P., F. Rovegno, D. Mooney, et al. Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization [J]. Gene, 2007. 401(1-2): p. 12-8.
[137]. Grutzmann, R., H. Boriss, O. Ammerpohl, et al. Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes [J]. Oncogene, 2005. 24(32): p. 5079-88.
[138]. Rhodes, D.R., J. Yu, K. Shanker, et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression [J]. Proc Natl Acad Sci U S A, 2004. 101(25): p. 9309-14.
[139]. Gunderson, K.L., F.J. Steemers, G. Lee, et al. A genome-wide scalable SNP genotyping assay using microarray technology [J]. Nat Genet, 2005. 37(5): p. 549-54.
[140]. Burton, P.R., D.G. Clayton, L.R. Cardon, et al. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants [J]. Nat Genet, 2007. 39(11): p. 1329-37.
[141]. Carter, N.P. Methods and strategies for analyzing copy number variation using DNA microarrays [J]. Nat Genet, 2007. 39(7 Suppl): p. S16-21.
[142]. Lee, C., A.J. Iafrate, A.R. Brothman. Copy number variations and clinical cytogenetic diagnosis of constitutional disorders [J]. Nat Genet, 2007. 39(7 Suppl): p. S48-54.
[143]. Manikandan, J., J.J. Aarthi, S.D. Kumar, et al. Oncomirs: The potential role of non-coding microRNAs in understanding cancer [J]. Bioinformation, 2008. 2(8): p. 330-4.
[144]. Grosshans, H., W. Filipowicz. Molecular biology: the expanding world of small RNAs [J]. Nature, 2008. 451(7177): p. 414-6.
[145]. Yin, J.Q., R.C. Zhao, K.V. Morris. Profiling microRNA expression with microarrays [J]. Trends Biotechnol, 2008. 26(2): p. 70-6.
[146]. Liu, C.G., G.A. Calin, S. Volinia, et al. MicroRNA expression profiling using microarrays [J]. Nat Protoc, 2008. 3(4): p. 563-78.
[147]. Barad, O., E. Meiri, A. Avniel, et al. MicroRNA expression detected by oligonucleotide microarrays: system establishment and expression profiling in human tissues [J]. Genome Res, 2004. 14(12): p. 2486-94.
[148]. Blenkiron, C., L.D. Goldstein, N.P. Thorne, et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype [J]. Genome Biol, 2007. 8(10): p. R214.
[149]. Yanaihara, N., N. Caplen, E. Bowman, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis [J]. Cancer Cell, 2006. 9(3): p. 189-98.
[150]. Mattie, M.D., C.C. Benz, J. Bowers, et al. Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies [J]. Mol Cancer, 2006. 5: p. 24.
[151]. Xiao, H.S., Q.H. Huang, F.X. Zhang, et al. Identification of gene expression profile of dorsal root ganglion in the rat peripheral axotomy model of neuropathic pain [J]. Proc Natl Acad Sci U S A, 2002. 99(12): p. 8360-5.
[152].范德刚,范清宇,张惠中等.利用基因芯片技术筛选不同转移能力骨肉瘤细胞差异表达基因[J].第四军医大学学报, 2003(09).
[153].王新颖,姜泊,赖卓胜等.基因芯片筛选大肠侧向发育型肿瘤相关基因的初步研究[J].第一军医大学学报, 2004(09).
[154].陈伟,付小兵,葛世丽等.寡核苷酸基因芯片筛选早期和晚期妊娠胎鼠皮肤内差异表达基因[J].中华医学杂志, 2005(07).
[155].郝希山,冯玉梅,张亮等.采用单引物扩增法标记的基因芯片技术筛选乳腺原发癌与淋巴结转移癌的差异表达基因[J].中华医学杂志, 2005(06).
[156].林嘉颖,杨学宁,杨矜记等.基因芯片筛选同时参与肺腺癌不同癌变进程的分子靶标[J].实用癌症杂志, 2005(03).
[157].宋波,唐建武,王波等.基因芯片筛选小鼠肝癌淋巴道转移相关基因[J].癌症, 2005(07).
[158].贺松琴,侯建国,谭晓洁等.应用基因芯片技术筛选前列腺癌转移相关基因[J].第二军医大学学报, 2006(11).
[159].雷平,杨树源,张建宁等.利用寡核苷酸基因芯片筛选人脑创伤的差异表达基因[J].中华实验外科杂志, 2006(12).
[160].石忠松,李明昌,孙毅明等.基因芯片筛选颅内动脉瘤炎性相关基因的研究[J].中华实验外科杂志, 2006(01).
[161].许沈华,余传定,牟瀚舟等.用基因芯片筛选胃黏膜癌变相关基因[J].中华胃肠外科杂志, 2006(05).
[162].张文晶,李红霞,宋磊等.应用基因芯片技术筛选卵巢癌紫杉醇耐药差异表达基因[J].肿瘤防治研究, 2006(01).
[163].郭遂群,邢福祺,庞战军等.基因芯片筛选的CFTR基因作为雌激素依赖型子宫内膜癌预后标志物的研究[J].广东医学, 2007(03).
[164].刘娟,姚树坤,殷飞.应用基因芯片筛选人肝癌、肝硬化及正常肝组织中差异表达的基因[J].基础医学与临床, 2007(01).
[165].周向东,钱桂生,刘凌志.采用基因芯片技术筛选人小细胞肺癌SH77/CDDP多药耐药相关基因表达谱的研究[J].第三军医大学学报, 2007(04).
[166].肖敏敏.基因芯片对白血病和淋巴瘤的分类和研究[J].白血病.淋巴瘤, 2002(02).
[167].黄庆,府伟灵,周玉等.基因芯片对人乳头瘤病毒的快速检测和分型[J].中华医院感染学杂志, 2005(04).
[168].雷平,杨树源,张建宁等.利用分类表达谱基因芯片筛选人脑创伤的差异表达基因[J].中华神经医学杂志, 2005(10).
[169].陈家林,邓超干,许晓清等.基因芯片对尖锐湿疣患者HPV的检测和基因分型[J].河北医学, 2006(02).
[170].赵敏,董汉生,陈雷等.尖锐湿疣组织HPV基因芯片分型及型别分析[J].中国皮肤性病学杂志, 2006(08).
[171].邹健,冉志华,萧树东.分类表达谱基因芯片在医学研究中的应用[J].肿瘤防治研究, 2006(03).
[172].荆志伟,王忠,王永炎等.基因芯片数据分析方法研究进展[J].生物技术通讯, 2007(01).
[173]. Bentley, D.R., S. Balasubramanian, H.P. Swerdlow, et al. Accurate whole human genome sequencing using reversible terminator chemistry [J]. Nature, 2008. 456(7218): p. 53-9.
[174]. Hillier, L.W., G.T. Marth, A.R. Quinlan, et al. Whole-genome sequencing and variant discovery in C. elegans [J]. Nat Methods, 2008. 5(2): p. 183-8.
[175]. Wang, J., W. Wang, R. Li, et al. The diploid genome sequence of an Asian individual [J]. Nature, 2008. 456(7218): p. 60-5.
[176]. Mortazavi, A., B.A. Williams, K. McCue, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq [J]. Nat Methods, 2008. 5(7): p. 621-8.
[177]. Sugarbaker, D.J., W.G. Richards, G.J. Gordon, et al. Transcriptome sequencing of malignant pleural mesothelioma tumors [J]. Proc Natl Acad Sci U S A, 2008. 105(9): p. 3521-6.
[178]. Zhao, T., G. Li, S. Mi, et al. A complex system of small RNAs in the unicellular green alga Chlamydomonas reinhardtii [J]. Genes Dev, 2007. 21(10): p. 1190-203.
[179]. Stark, A., P. Kheradpour, L. Parts, et al. Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes [J]. Genome Res, 2007. 17(12): p. 1865-79.
[180]. Barski, A., S. Cuddapah, K. Cui, et al. High-resolution profiling of histone methylations in thehuman genome [J]. Cell, 2007. 129(4): p. 823-37.
[181]. Grant, G.R., E. Manduchi, C.J. Stoeckert, Jr. Analysis and management of microarray gene expression data [J]. Curr Protoc Mol Biol, 2007. Chapter 19: p. Unit 19 6.
[182]. Stoeckert, C.J., Jr., H.C. Causton, C.A. Ball. Microarray databases: standards and ontologies [J]. Nat Genet, 2002. 32 Suppl: p. 469-73.
[183]. Cordero, F., M. Botta, R.A. Calogero. Microarray data analysis and mining approaches [J]. Brief Funct Genomic Proteomic, 2007. 6(4): p. 265-81.
[184].李瑶,基因芯片数据分析与处理[M]. 2006,北京:化学工业出版社.
[185]. Rousseeuw, P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis [J]. Journal of Computational Applications in Math, 1987(20): p. 53-65.
[186]. Ashburner, M., C.A. Ball, J.A. Blake, et al. Gene Ontology: tool for the unification of biology [J]. Nature Genetics, 2000. 25(1): p. 25-29.
[187]. Aoki, K.F., M. Kanehisa. Using the KEGG database resource [J]. Curr Protoc Bioinformatics, 2005. Chapter 1: p. Unit 1 12.
[188]. Kanehisa, M. The KEGG database [J]. Novartis Found Symp, 2002. 247: p. 91-101; discussion 101-3, 119-28, 244-52.
[189]. Kanehisa, M., M. Araki, S. Goto, et al. KEGG for linking genomes to life and the environment [J]. Nucleic Acids Res, 2008. 36(Database issue): p. D480-4.
[190]. Kanehisa, M., S. Goto. KEGG: kyoto encyclopedia of genes and genomes [J]. Nucleic Acids Res, 2000. 28(1): p. 27-30.
[191]. Kanehisa, M., S. Goto, M. Hattori, et al. From genomics to chemical genomics: new developments in KEGG [J]. Nucleic Acids Res, 2006. 34(Database issue): p. D354-7.
[192]. Kanehisa, M., S. Goto, S. Kawashima, et al. The KEGG databases at GenomeNet [J]. Nucleic Acids Res, 2002. 30(1): p. 42-6.
[193]. Kanehisa, M., S. Goto, S. Kawashima, et al. The KEGG resource for deciphering the genome [J]. Nucleic Acids Res, 2004. 32(Database issue): p. D277-80.
[194]. Barrett, T., D.B. Troup, S.E. Wilhite, et al. NCBI GEO: mining tens of millions of expression profiles--database and tools update [J]. Nucleic Acids Res, 2007. 35(Database issue): p. D760-5.
[195]. Edgar, R., T. Barrett. NCBI GEO standards and services for microarray data [J]. Nat Biotechnol, 2006. 24(12): p. 1471-2.
[196]. Saeed, A.I., N.K. Bhagabati, J.C. Braisted, et al. TM4 microarray software suite [J]. Methods Enzymol, 2006. 411: p. 134-93.
[197]. Saeed, A.I., V. Sharov, J. White, et al. TM4: a free, open-source system for microarray data management and analysis [J]. Biotechniques, 2003. 34(2): p. 374-8.
[198]. Durinck, S., Y. Moreau, A. Kasprzyk, et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis [J]. Bioinformatics, 2005. 21(16): p. 3439-40.
[199]. Zhao, Y., R. Simon. BRB-ArrayTools Data Archive for Human Cancer Gene Expression: A Unique and Efficient Data Sharing Resource [J]. Cancer Inform, 2008. 6: p. 9-15.
[200]. Huang da, W., B.T. Sherman, Q. Tan, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists [J]. Nucleic Acids Res, 2007. 35(Web Server issue): p. W169-75.
[201]. Schramm, A., J.H. Schulte, L. Klein-Hitpass, et al. Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling [J]. Oncogene, 2005. 24(53): p.7902-12.
[202]. Lee, Y., C.K. Lee. Classification of multiple cancer types by multicategory support vector machines using gene expression data [J]. Bioinformatics, 2003. 19(9): p. 1132-9.
[203]. Pavlidis, P., J. Weston, J. Cai, et al. Learning gene functional classifications from multiple data types [J]. J Comput Biol, 2002. 9(2): p. 401-11.
[204]. Man, M.Z., G. Dyson, K. Johnson, et al. Evaluating methods for classifying expression data [J]. J Biopharm Stat, 2004. 14(4): p. 1065-84.
[205]. Blazadonakis, M.E., M. Zervakis. Wrapper filtering criteria via linear neuron and kernel approaches [J]. Comput Biol Med, 2008. 38(8): p. 894-912.
[206]. Le Cao, K.A., O. Goncalves, P. Besse, et al. Selection of biologically relevant genes with a wrapper stochastic algorithm [J]. Stat Appl Genet Mol Biol, 2007. 6: p. Article29.
[207].张学工,统计学习理论的本质[M]. 1999,北京:清华大学出版社.
[208].许建华,张学工译,统计学习理论[M]. 2004,北京:电子工业出版社.
[209]. Harris, M.A., J. Clark, A. Ireland, et al. The Gene Ontology (GO) database and informatics resource [J]. Nucleic Acids Research, 2004. 32: p. D258-D261.
[210]. Resnik, P. Semantic similarity in a taxonomy: an information-based measure an its application to problems of ambiguity in natural language [J]. J. Artificial Intelligence Res., 1999(11): p. 95-130.
[211]. Sevilla, J.L., V. Segura, A. Podhorski, et al. Correlation between gene expression and GO semantic similarity [J]. Ieee-Acm Transactions on Computational Biology and Bioinformatiocs, 2005. 2(4): p. 330-338.
[212]. Jiang, J., D. Conrath, Semantic similarity Based on Corpus Statistics and Lexical Taxonomy, in tenth International Conference on Research on Computational Linguistics(ROCLING X). 1997: Taiwan.
[213]. Lin, D. An Information-Theoretic Definition of Similarity. in Proc. 15th International conference Machine Learing. 1998.
[214]. Couto, F.M., M.J. Silva, P.M. Coutinho. Measuring semantic similarity between Gene Ontology terms [J]. Data & Knowledge Engineering, 2007. 61(1): p. 137-152.
[215]. Lee, S.G., J.U. Hur, Y.S. Kim. A graph-theoretic modeling on GO space for biological interpretation of gene clusters [J]. Bioinformatics, 2004. 20(3): p. 381-388.
[216]. Jiang, J., D. Conrath. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. in tenth international Conference on Research on Computational Linguistics. 1997.
[217]. Wang, J.Z., Z.D. Du, R. Payattakool, et al. A new method to measure the semantic similarity of GO terms [J]. Bioinformatics, 2007. 23(10): p. 1274-1281.
[218]. Resnik, P., Using information Content to evaluate semantic similarity in taxonomy, in Proc. 14th Int'l Joint Conf. Artificial Intelligence. 1995. p. 448-453.
[219]. DeRisi, J.L., V.R. Iyer, P.O. Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale [J]. Science, 1997. 278(5338): p. 680-6.
[220]. Valentini, G. Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles [J]. Artif Intell Med, 2002. 26(3): p. 281-304.
[221]. Yu, S.L., H.Y. Chen, G.C. Chang, et al. MicroRNA signature predicts survival and relapse in lung cancer [J]. Cancer Cell, 2008. 13(1): p. 48-57.
[222]. Calin, G.A., M. Ferracin, A. Cimmino, et al. A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia [J]. N Engl J Med, 2005. 353(17): p. 1793-801.
[223]. Takamizawa, J., H. Konishi, K. Yanagisawa, et al. Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival [J]. Cancer Res, 2004. 64(11): p. 3753-6.
[224]. Iorio, M.V., M. Ferracin, C.G. Liu, et al. MicroRNA gene expression deregulation in human breast cancer [J]. Cancer Res, 2005. 65(16): p. 7065-70.
[225]. Bloomston, M., W.L. Frankel, F. Petrocca, et al. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis [J]. JAMA, 2007. 297(17): p. 1901-8.
[226]. Parkin, D.M., F. Bray, J. Ferlay, et al. Global cancer statistics, 2002 [J]. CA Cancer J Clin, 2005. 55(2): p. 74-108.
[227]. Parkin, D.M. Global cancer statistics in the year 2000 [J]. Lancet Oncol, 2001. 2(9): p. 533-43.
[228]. Ke, A.W., G.M. Shi, J. Zhou, et al. Role of overexpression of CD151 and/or c-Met in predicting prognosis of hepatocellular carcinoma [J]. HEPATOLOGY, 2009. 49(2): p. 491-503.
[229]. Ye, Q.H., L.X. Qin, M. Forgues, et al. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning [J]. Nat Med, 2003. 9(4): p. 416-23.
[230]. Budhu, A., H.L. Jia, M. Forgues, et al. Identification of metastasis-related microRNAs in hepatocellular carcinoma [J]. HEPATOLOGY, 2008. 47(3): p. 897-907.
[231]. Zhao, A., K. Dou, K. Li, et al. [The effects of the expression of VEGF and KDR on the angiogenesis, growth and metastasis of hepatocellular carcinoma] [J]. Zhonghua Wai Ke Za Zhi, 2000. 38(6): p. 453-6.
[232]. Iguchi, H., M. Yokota, M. Fukutomi, et al. A possible role of VEGF in osteolytic bone metastasis of hepatocellular carcinoma [J]. J Exp Clin Cancer Res, 2002. 21(3): p. 309-13.
[233]. Meng, C., X. Chen. [Association of VEGF, uPA, ICAM-1 and PCNA expression with metastasis and recurrence in hepato cellular carcinoma] [J]. Zhonghua Wai Ke Za Zhi, 2002. 40(9): p. 673-5.
[234]. Hirohashi, K., T. Yamamoto, T. Uenishi, et al. CD44 and VEGF expression in extrahepatic metastasis of human hepatocellular carcinoma [J]. Hepatogastroenterology, 2004. 51(58): p. 1121-3.
[235]. Tsai, Y.C., A. Mendoza, J.M. Mariano, et al. The ubiquitin ligase gp78 promotes sarcoma metastasis by targeting KAI1 for degradation [J]. Nat Med, 2007. 13(12): p. 1504-9.
[236]. Qi, J., K. Nakayama, S. Gaitonde, et al. The ubiquitin ligase Siah2 regulates tumorigenesis and metastasis by HIF-dependent and -independent pathways [J]. Proc Natl Acad Sci U S A, 2008. 105(43): p. 16713-8.