“证候-基因组”的方法学及家系虚寒证的代谢基因表达谱研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
背景
     人类基因组计划的完成和后基因组时代的到来为中医证候的研究带来了新的机遇,如何实现“证候-基因组学”阐释是目前中医药研究的热点,但“证候-基因组”研究面临着诸多的困惑,故“证候-基因组”的方法学研究对于解决目前研究中面临的困惑具有积极的意义。墨守成规与妄自菲薄都不是科学的态度,本着实事求是的治学精神,为“证候-基因组”研究探索新路。
     目的
     ① 建立“证候-基因组”研究的方法学平台;
     ② 研究家系虚寒证的差异基因表达谱。
     方法
     ① 理论:系统整理国外基因芯片文献,从理论的角度对基因芯片的实验流程和数据分析进行了系统的分析,为正确应用基因芯片技术提供理论平台。以OMIM数据库为基础,以中医整体观和天人相应观为指导思想,以现代医学模式为参照,建立中西医结合基因模糊分析系统,为“证候-基因表达谱”研究提供临床应用的信息挖掘平台。即从理论的角度对“证候-基因表达谱”进行方法学探讨。
     ② 实验:从医学的异病同证、遗传学的家系、分子生物学的基因表达、数学上的生物统计、生物信息学的信息挖掘等多学科结合,精选一个家系虚寒证为研究对象,采用直接、间接比较和以药测证3种实验方案,研究虚寒证的差异基因表达谱。以间接比较的差异表达基因为基础建立虚寒证的支持向量机判别模型,同时应用RT-PCR和qPCR的方法对差异表达基因进行临床验证。即从实证的角度对“证候-基因表达谱”进行方法学探讨。
     结果
     ① 系统地分析了实验设计和芯片实验的影响因素,以及21种数据分析方法的优缺点,在此基础上提出选择“证候-基因表达谱”实验方案的建设性意见。
     ② 建立了中西医结合基因模糊分析系统,该系统是以主题词检索OMIM数据库而建立的本地数据库,其包括现代医学疾病410种,中医证候等词条80条,遗传相关50条,基因性质相关150条,环境相关200条,心理相关150条,社会相关30条,其它130条,共1200条。
BackgroundAfter the Human Genome Project to be completed, the arrival of post genome era provide an opportunity to study the Zheng of Traditional Chinese Medicine (TCM). At present, the important focuses of a research is to explore "Zheng-genomics", in which "Zheng-gene expression profile" confronts a great deal of puzzlements. Therefore, the methodology study on "Zheng-gene expression profile" has positive meanings to clarify therse problemes. It is not scientific attitudes that neither stick to established practices, nor underestimate oneself. We should explore a new way for the study of "Zheng-gene expression profile" in line with the spirit of seeking truth from fact.Objective① To establish methodology platform on "Zheng-genomics".② To researtch gene expression profile on predigree in deficiency-cold Zheng.Methods① Through the reviewing the up-dated references in the world on microarray technology systematically and data analysis, A theoretical fundamental for correct usage of microarray technology may be provided. A fuzzy analysis system on gene functions is set up by combining between traditional Chinese and western medicine from OMIM database. This system adopts the concept of integrity and regards the interaction between man and nature as guidance and refer to modern medicine pattern also. It provides an information platform for clinical application in gene expression related.② According to the conception of different diseases may being same Zheng in medicine, the pedigree in genetics, the gene expression in molecular biology, the biologic statistics on mathematics and information mining of bioinformatics, we adopt 3 kind of experiment schemes to research one pedigree of deficiency-cold Zheng. A justment model of support vector machines is applied successfully for the Zheng by the result of indirect compareing. At the same time, we verificated the difference expression gene by the methods of RT-PCR and qPCR.Results① Through the analyzing the influence agents of experiment systematically, the advantages and shortcomings of the design and 21 kind of mathmatics methods were check out. A constructive suggestion is put forward for choosing an experiment scheme on "Zheng-gene expression profile".② A fuzzy analysis system of combination between traditional Chinese and
引文
[1] 中医现代化科技发展战略研究课题组.中医现代化重大基础理论研究与重点任务.世界科学技术-中药现代化,2001,3(6):1-6
    [2] 王米渠,吴斌,严石林,等.论虚寒证基因芯片及生物信息的高起点切入研究.辽宁中医杂志,2003,30(3):166-9
    [3] 梁茂新,刘进,洪治平,等.中医证研究的困惑与对策(M).北京:人民卫生出版社,2000,7-40
    [4] 沈自尹.对中医基础理论研究的思路.中国中西医结合杂志.1997.17(11):643-44
    [5] Cavalli-Sforza LL.The Human Genome Diversity Project: past, present and future.Nat Rev Genet.2005,6(4):333-40
    [6] Hirschhom JN, Daly MJ.Genome-wide association studies for common diseases and complex traits.Nat Rev Genet.2005,6(2):95-108.
    [7] 王米渠,许锦文,林乔,等.中医研究与基因组学及基因芯片技术.江西中医学院学报.2002,14(3):1-2
    [8] 杨焕明.基因组学—中医药学现代化的一个切入点.医药世界,2000,(8):5-7
    [9] 宋为民,王明艳.基因组学是中医现代化的最佳切入点.南京中医药大学学报,1999,15(4):193-5
    [10] 国家中医药管理局科教司.中医药与基因组学研讨会纪要.世界科学技术—中药现代化,1999,1(4):67-8
    [11] 金光亮.证候基因组学和证候蛋白质组学浅论.中国医药学报,2003,18(6):332-5
    [12] 姚魁武,王阶.中医证候实质研究的现状与思考述评.中医药学刊,2003,21(9):1494-5
    [13] 林乔,王米渠,吴斌.中医理论与人类基因组的研究.中医药学刊,2003,21(8):1232-6
    [14] 王米渠,许锦文,林乔.分子中医学发展三论.现代中西医结合杂志,2003,12(1):1-2
    [15] 王米渠,吴斌,冯韧,等.论“证”有分子生物学基础的假说.现代中西医结合杂志.2003,12(3):225-6
    [16] 骆文斌,吴承玉.人类基因组学与中医学理论相关性探讨.南京中医药大学学报(自然科学版),2002,18(2):82-4
    [17] Huddleston HG, Wong KK, Welch WR, et al. Clinical applications of microarray technology: creatine kinase B is an up-regulated gene in epithelial ovarian cancer and shows promise as a serum marker. Gynecol Oncol, 2005,96(1):77-83.
    [18] Onda M, Emi M, Nagai H, et al. Gene expression patterns as marker for 5-year postoperative prognosis of primary breast cancers. J Cancer Res Clin Oncol, 2004,130(9):537-45.
    [19] 朱姝,高荣林,隋殿军.基因组学、蛋白质组学与证候实质.中国中医基础医学杂志,2002,8(12):19-20
    [20] 王忠,王阶,王永炎.后基因组时代中医证候组学研究的思考.中国中西医结合杂志,2001,21(8):621-3
    [21] 吴斌,黄信勇,王米渠,等.运用基因芯片研究骨关节炎虚寒证的基因表达谱述要.中国中医药学刊,2004,22(11):2008-10
    [22] 王米渠,冯韧,严石林,等.基因表达谱芯片与中医寒证的7类相关基因.中医杂志.2003,44(4):288-9
    [23] Online Mendelian Inheritance in Man, OMIM (TM). McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University (Baltimore, MD) and National Center for Biotechnology Information, National Library of Medicine (Bethesda, MD), 2000. World Wide Web URL: http://www.ncbi.nlm.nih.gov/omim/
    [24] Fodor SP, Read JL, Pirrung MC, et al. Light-directed, spatially addressable parallel chemical synthesis. Science, 1991,251(4995):767-73.
    [25] Mirzabekov AD. DNA sequencing by hybridization~a megasequencing method and a diagnostic tool?. Trends Biotechnol, 1994,12(1):27-32.
    [26] Schena M, Shalon D, Davis RW, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995,270(5235):467-70.
    [27] Pease AC, Solas D, Sullivan EJ, et al. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci U S A, 1994,91(11):5022-6.
    [28] Schena M, Heller RA, Theriault TP, et al. Microarrays: biotechnology's discovery platform for functional genomics. Trends Biotechnol, 1998,16(7):301-6.
    [29] Foster WR, Huber RM. Current themes in microarray experimental design and analysis. Drug Discov Today. 2002,7(5):290-2.
    [30] Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet, 2002.32 Suppl:490-5.
    [31] Pichler FB, Black MA, Williams LC, et al. Design, normalization, and analysis of spotted microarray data. Methods Cell Biol, 2004,77:521-43.
    [32] Murphy D. Gene expression studies using microarrays: principles, problems, and prospects. Adv Physiol Educ, 2002,26(1-4):256-70.
    [33] Hedenfalk I, Duggan D, Chen Y, et al. Gene-expression profiles in hereditary breast cancer. N Engl J Med, 2001,344(8):539-48.
    [34] Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 1999,286(5439):531-7.
    [35] Ross DT, Scherf U, Eisen MB, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet, 2000,24(3):227-35.
    [36] Lorenz MG, Cortes LM, Lorenz JJ, et al. Strategy for the design of custom cDNA microarrays. Biotechniques, 2003,34(6): 1264-70.
    [37] Simon R, Radmacher MD, Dobbin K. Design of studies using DNA microarrays. Genet Epidemiol. 2002,23(1):21-36.
    [38] Draghici S, Kuklin A, Hoff B, et al. Experimental design, analysis of variance and slide quality assessment in gene expression arrays. Curr Opin Drug Discov Devel, 2001,4(3):332-7.
    [39] Kerr MK, Churchill GA. Experimental design for gene expression microarrays. Biostatistics, 2001,2(2):183-201.
    [40] Dobbin K, Simon R. Comparison of microarray designs for class comparison and class discovery. Bioinformatics, 2002,18( 11): 1438-45.
    [41] Yang YH, Speed T. Design issues for cDNA microarray experiments. Nat Rev Genet, 2002,3(8):579-88.
    [42] Glonek GF, Solomon PJ. Factorial and time course designs for cDNA microarray experiments. Biostatistics, 2004,5(1):89-111.
    [43] Townsend JP. Multifactorial experimental design and the transitivity of ratios with spotted DNA microarrays. BMC Genomics, 2003,4(1 ):41.
    [44] Kerr MK. Design considerations for efficient and effective microarray studies. Biometrics, 2003,59(4):822-8.
    [45] McShane LM, Shih JH, Michalowska AM. Statistical issues in the design and analysis of gene expression microarray studies of animal models. J Mammary Gland Biol Neoplasia, 2003,8(3):359-74.
    [46] Kendziorski CM, Zhang Y, Lan H, et al. The efficiency of pooling mRNA in microarray experiments. Biostatistics, 2003,4(3):465-77.
    [47] Shih JH, Michalowska AM, Dobbin K, et al. Effects of pooling mRNA in microarray class comparisons. Bioinformatics, 2004,20(18):3318-25.
    [48] Yang MC, Yang JJ, Mclndoe RA, et al. Microarray experimental design: power and sample size considerations. Physiol Genomics, 2003,16(l):24-8.
    [49] Lee ML, Whitmore GA. Power and sample size for DNA microarray studies. Stat Med, 2002,21(23): 3543-70.
    [50] Hwang D, Schmitt WA, Stephanopoulos G, et al. Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics, 2002,18(9):l 184-93.
    [51] Heller MJ. DNA microarray technology: devices, systems, and applications. Annu Rev Biomed Eng, 2002,4:129-53.
    [52] Wurmbach E, Yuen T, Sealfon SC. Focused microarray analysis. Methods, 2003,31(4):306-16.
    [53] Nguyen DV, Arpat AB, War.g N, et al. DNA microarray experiments: biological and technological aspects. Biometrics, 2002,58(4):701-17.
    [54] Hegde P, Qi R, Abernathy K, et al. A concise guide to cDNA microarray analysis. Biotechniques, 2000,29(3):548-50, 552 4, 556 passim.
    [55] Li L, Ma W, Zheng W, et al. Progress in DNA chip technology. Chin Med Sci J, 2001,16(l):59-62.
    [56] Zubritsky E. Spotting a microarray system. Anal Chem, 2000,72(23):761 A-767A.
    [57] Iacobas AD, Urban M, Spray DC. New protocol in spotting microarray technique. Rom J Physiol, 2000,37(1-4):69-30.
    [58] Tang Y, Gilbert DL, Glauser TA, et al. Blood gene expression profiling of neurologic diseases: a pilot microarray study. Arch.Neurol, 2005,62(2):210-5.
    [59] Tanner MA, Berk LS, Felten DL, et al. Substantial changes in gene expression level due to the storage temperature and storage duration of human whole blood. Clin Lab Haematol, 2002,24(6):337-41.
    [60] Grotzer MA, Parti R, Geoerger B, et al. Biological stability of RNA isolated from RNAlater-treated brain tumor and neuroblastoma xenografts. Med Pediatr Oncol, 2000,34(6):438-42.
    [61] Zhao H, Hastie T, Whitfield ML, et al. Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis. BMC Genomics, 2002,3(l):31.
    [62] Feldman AL, Costouros NG, Wang E, et al. Advantages of mRNA amplification for microarray analysis. Biotechniques, 2002,33(4):906-12, 914.
    [63] Luzzi V, Mahadevappa M, Raja R, et al. Accurate and reproducible gene expression profiles from laser capture microdissection, transcript amplification, and high density oligonucleotide microarray analysis. J Mol Diagn, 2003,5(1):9-14.
    [64] (?)uskas LG, Zvara A, Hackler L Jr, et al. RNA amplification results in reproducible microarray data with slight ratio bias. Biotechniques, 2002,32(6): 1330-4,1336, 1338, 1340.
    [65] Gomes LI, Silva RL, Stolf BS, et al. Comparative analysis of amplified and nonamplified RNA for hybridization in cDNA microarray. Anal Biochem, 2003,321(2):244-51.
    [66] Li Y, Ali S, Philip PA, et al. Direct comparison of microarray gene expression profiles between non-amplification and a modified cDNA amplification procedure applicable for needle biopsy tissues. Cancer Detect Prev, 2003,27(5):405-11.
    [67] Smith L, Underhill P, Pritchard C, et al. Single primer amplification (SPA) of cDNA for microarray expression analysis. Nucleic Acids Res, 2003,31(3):e9.
    [68] Xiang CC, Chen M, Ma L, et al. A new strategy to amplify degraded RNA from small tissue samples for microarray studies. Nucleic Acids Res, 2003,31(9):e53.
    [69] Pabon C, Modrusan Z, Ruvolo MV, et al. Optimized T7 amplification system for microarray analysis. Biotechniques, 2001,31(4):874-9.
    [70] Stirewalt DL, Pogosova-Agadjanyan EL, Khalid N, et al. Single-stranded linear amplification protocol results in reproducible and reliable microarray data from nanogram amounts of starting RNA. Genomics. 2004,83(2):321-31.
    [71] Xiang CC, Chen M, Kozhich OA, et al. Probe generation directly from small numbers of cells for DNA microarray studies. Biotechniques, 2003,34(2):386-8, 390, 392-3.
    [72] Spiess AN, Mueller N, Ivell R. Amplified RNA degradation in T7-amplification methods results in biased microarray hybridizations. BMC Genomics, 2003,4(1):44.
    [73] Schneider J, Buness A, Huber W, et al. Systematic analysis of T7 RNA polymerase based in vitro linear RNA amplification for use in microarray experiments. BMC Genomics, 2004,5(1):29.
    [74] Petalidis L, Bhattacharyya S, Morris GA, et al. Global amplification of mRNA by template-switching PCR: linearity and application to microarray analysis. Nucleic Acids Res, 2003,31(22):el42.
    [75] Wang J, Hu L, Hamilton SR, et al. RNA amplification strategies for cDNA microarray experiments. Biotechniques, 2003,34(2):394-400.
    [76] Yu J, Othman MI, Farjo R, et al. Evaluation and optimization of procedures for target labeling and hybridization of cDNA microarrays. Mol Vis, 2002,8:130-7.
    [77] Cole K, Truong V, Barone D, et al. Direct labeling of RNA with multiple biotins allows sensitive expression profiling of acute leukemia class predictor genes. Nucleic Acids Res, 2004,32( 11 ):e86.
    [78] Gupta V, Cherkassky A, Chatis P, et al. Directly labeled mRNA produces highly precise and unbiased differential gene expression data. Nucleic Acids Res, 2003,31(4):e13.
    [79] Beier V, Bauer A, Baum M, et al. Fluorescent sample labeling for DNA microarray analyses. Methods Mol Biol, 2004,283:127-35.
    [80] Richter A, Schwager C, Hentze S, et al. Comparison of fluorescent tag DNA labeling methods used for expression analysis by DNA microarrays. Biotechniques, 2002,33(3):620-8, 630.
    [81] Nilsen TW, Grayzel J, Prensky W. Dendritic nucleic acid structures. J Theor Biol, 1997,187(2):273-84.
    [82] Manduchi E, Scearce LM, Brestelli JE, et al. Comparison of different labeling methods for two-channel high-density microarray experiments. Physiol Genomics, 2002,10(3):169-79.
    [83] Badiee A, Eiken HG, Steen VM, et al. Evaluation of five different cDNA labeling methods for microarrays using spike controls. BMC Biotechnol, 2003,3(l):23.
    [84] Freeman WM, Robertson DJ, Vrana KE. Fundamentals of DNA hybridization arrays for gene expression analysis. Biotechniques, 2000,29(5): 1042-6, 1048-55.
    [85] Sartor M, Schwanekamp J, Halbleib D, et al. Microarray results improve significantly as hybridization approaches equilibrium. Biotechniques, 2004,36(5):790-6.
    [86] McQuain MK, Seale K, Peek J, et al. Chaotic mixer improves microarray hybridization. Anal Biochem, 2004,325(2):215-26.
    [87] Franssen-van Hal NL, Vorst O, Kramer E, et al. Factors influencing cDNA microarray hybridization on silylated glass slides. Anal Biochem, 2002,308(l):5-17.
    [88] Kuklin A, Shams S, Shah S. High throughput screening of gene expression signatures. Genetica, 2000,108(1):41-6.
    [89] Brignac SJ Jr, Gangadharan R, McMahon M, et al. A proximal CCD imaging system for high-throughput detection of microarray-based assays. IEEE Eng Med Biol Mag, 1999,18(2): 120-2.
    [90] Graves DJ, Su HJ, Addya S, et al. Four-laser scanning confocal system for microarray analysis. Biotechniques, 2002,32(2):346-8, 350, 352, 354.
    [91] Khomyakova EB, Dreval EV, Tran-Dang M, et al. Innovative instrumentation for microarray scanning and analysis: application for characterization of oligonucleotide duplexes behavior. Cell Mol Biol (Noisy-le-grand), 2004,50(3):217-24.
    [92] Lyng H, Badiee A, Svendsrud DH, et al. Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction. BMC Genomics, 2004,5(1): 10.
    [93] Romualdi C, Trevisan S, Celegato B, et al. Improved detection of differentially expressed genes in microarray experiments through multiple scanning and image integration. Nucleic Acids Res, 2003,31(23):el49.
    [94] Yang YH, Buckley MJ, Speed TP. Analysis of cDNA microarray images. Brief Bioinform, 2001,2(4):341-9.
    [95] Jain AN, Tokuyasu TA, Snijders AM, et al. Fully automatic quantification of microarray image data. Genome Res, 2002,12(2):325-32.
    [96] Marzolf B, Johnson MH. Validation of microarray image analysis accuracy. Biotechniques, 2004,36(2):304-8.
    [97] Angulo J, Serra J. Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics, 2003,19(5):553-62.
    [98] Adams, R., and Bischof, L. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence.1994,16,641-647.
    [99] Eisen, M. B. (1999). ScanAlyze User Manual. Stanford University, Palo Alto, http://rana.lbl.gov.
    [100] Array Vision, Imaging Research Inc. http://imaging.brocku.ca.
    [101]Buckley, M. J. (2000). Spot User's Guide. CSIRO Mathematical and Information Sciences, Sydney, Australia. http://www.cmis.csiro.au/iap/Spot/spotmanual.htm.
    [102] Wang X, Ghosh S, Guo SW. Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res, 2001,29(15):E75-5.
    [103]Soille, P. (1999). Morphological Image Analysis: Principles and Applications. Springer,New York.
    [104]Yang, Y. H., Buckley, M. J., Dudoit, S., et al. Comparison of methods for image analysis on cDNA microarray data. Journal of Computational and Graphical Statistics. 2002,11, 108-136.
    [105]Wang X, Ghosh S, Guo SW. Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res, 2001,29(15):E75-5.
    [106]Kim JH, Kim HY, Lee YS. A novel method using edge detection for signal extraction from cDNA microarray image analysis. Exp Mol Med, 2001,33(2):83-8.
    [107]Korn EL, Habermann JK, Upender MB, et al. Objective method of comparing DNA microarray image analysis systems. Biotechniques, 2004,36(6):960-7.
    [108]Tseng GC, Oh MK, Rohlin L, et al. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res, 2001,29(12):2549-57.
    [109]Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics, 2001,17(6):520-5.
    [110]Zhou X, Wang X, Dougherty ER. Missing-value estimation using linear and non-linear regression with Bayesian gene selection. Bioinformatics, 2003,19(17):2302-7.
    [111]Oba S, Sato MA, Takemasa I, et al. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 2003,19(16):2088-96.
    [112]Bo TH, Dysvik B, Jonassen I. LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res, 2004,32(3):e34.
    [113]Smyth GK, Yang YH, Speed T. Statistical issues in cDNA microarray data analysis. Methods Mol Biol, 2003,224:111-36.
    [114]Ding Y, Wilkins D. The effect of normalization on microarray data analysis. DNA Cell Biol, 2004,23(10):635-42.
    [115]Quackenbush J. Microarray data normalization and transformation. Nat Genet, 2002,32 Suppl:496-501.
    [116]Chen YJ, Kodell R, Sistare F, et al. Normalization methods for analysis of microarray gene-expression data. J Biopharm Stat, 2003,13(l):57-74.
    [117]Smyth GK, Speed T. Normalization of cDNA microarray data. Methods, 2003,31 (4):265-73.
    [118]Leung YF, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends Genet, 2003,19(11):649-59.
    [119]Workman C, Jensen LJ, Jarmer H, et al. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol, 2002,3(9):research0048.
    [120]Wilson DL, Buckley MJ, Helliwell CA, et al. New normalization methods for cDNA microarray data. Bioinformatics, 2003,19(11):1325-32.
    [121]Kepler TB, Crosby L, Morgan KT. Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol, 2002,3(7):RESEARCH0037.
    [122]Bilban M, Buehler LK, Head S, et al. Normalizing DNA microarray data. Curr Issues Mol Biol, 2002,4(2):57-64.
    [123]Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol, 2000,7(6):819-37.
    [124]Yang YH, Dudoit S, Luu P, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res, 2002, 30(4):e15.
    [125]Faller D, Voss HU, Timmer J, et al. Normalization of DNA-microarray data by nonlinear correlation maximization. J Comput Biol, 2003,10(5):751-62.
    [126]Draghici S, Kulaeva O, Hoff B, et al. Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays. Bioinformatics, 2003,19(11):1348-59.
    [127]Pieler R, Sanchez-Cabo F, Hackl H, et al. ArrayNorm: comprehensive normalization and analysis of microarray data. Bioinformatics, 2004,20(12): 1971-3.
    [128]Hoffmann R, Seidl T, Dugas M. Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biol, 2002,3(7): RESEARCH0033.
    [129]Park T, Yi SG, Kang SH, et al. Evaluation of normalization methods for microarray data. BMC Bioinformatics, 2003,4(1):33.
    [130]Gerhold D, Lu M, Xu J, et al. Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays. Physiol Genomics, 2001,5(4): 161-70.
    [131]Mutch DM, Berger A, Mansourian R, et al. The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data. BMC Bioinformatics, 2002,3(1): 17.
    [132]Yang IV, Chen E, Hasseman JP, et al. Within the fold: assessing differential expression measures. and reproducibility in microarray assays. Genome Biol, 2002,3(11 ):research0062.
    [133]Black MA, Doerge RW. Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments. Bioinformatics, 2002,18(12):1609-16.
    [134]Cui X, Churchill GA. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol, 2003,4(4):210.
    [135]Draghici S. Statistical intelligence: effective analysis of high-density microarray data. Drug Discov Today, 2002,7(11 Suppl):SS5-63.
    [136]Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics, 2001,17(6):509-19.
    [137]Long AD, Mangalam HJ, Chan BY, et al. Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J Biol Chem, 2001,276(23):19937-44.
    [138]Pavlidis P. Using ANOVA for gene selection from microarray studies of the nervous system. Methods, 2003,31(4):282-9.
    [139]Hatfield GW, Hung SP, Baldi P. Differential analysis of DNA microarray gene expression data. Mol Microbiol, 2003,47(4):871-7.
    [140]Pan KH, Lih CJ, Cohen SN. Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge. Proc Natl Acad Sci USA, 2002,99(4):2118-23.
    [141]Aubert J, Bar-Hen A, Daudin J, et al. Correction: Determination of the differentially expressed genes in microarray experiments using local FDR. BMC Bioinformatics, 2005,6(l):42.
    [142]Zhao Y, Pan W. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments. Bioinformatics, 2003,19(9): 1046-54.
    [143]Troyanskaya OG, Garber ME, Brown PO, et al. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 2002,18(11):1454-61.
    [144]Efron B, Tibshlrani R. Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol, 2002,23(1):70-86,
    [145]Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA, 2001,98(9):5116-21.
    [146]Pan W, Lin J, Le CT. A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics, 2003,3(3): 117-24.
    [147]Strimmer K. Modeling gene expression measurement error: a quasi-likelihood approach. BMC Bioinformatics, 2003,4(1): 10.
    [148]Segal MR, Dahlquist KD, Conklin BR. Regression approaches for microarray data analysis. J Comput Biol, 2003,10(6):961-80.
    [149]L, H, Gui J. Partial Cox regression analysis for high-dimensional microarray gene expression data. B(?)oinformatics, 2004,20 Suppl 1:1208-1215.
    [150]Huang X, Pan W. Linear regression and two-class classification with gene expression data. Bioinformatics, 2003,19(16):2072-8.
    [151]Guess MJ, Wilson SB. Introduction to hierarchical clustering. J Clin Neurophysiol, 2002,19(2): 144-51.
    [152]Levenstien MA, Yang Y, Ott J. Statistical significance for hierarchical clustering in genetic association and microarray expression studies. BMC Bioinformatics, 2003,4(1):62.
    [153]Bertucci F, Salas S, Eysteries S, et al. Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters. Oncogene, 2004,23(7): 1377-91.
    [154]Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA, 2001,98(19): 10869-74.
    [155] Sherlock G. Analysis of large-scale gene expression data. Brief Bioinform, 2001,2(4):350-62.
    [156]D'ambrosio C, Akin C, Wu Y, et al. Gene expression analysis in mastocytosis reveals a highly consistent profile with candidate molecular markers. J Allergy Clin Immunol, 2003,112(6): 1162-70.
    [157]Steinley D. Local optima in K-means clustering: what you don't know may hurt you. Psychol Methods, 2003,8(3):294-304.
    [158]Toronen P, Kolehmainen M, Wong G, et al. Analysis of gene expression data using self-organizing maps. FEBS Lett, 1999,451(2):142-6.
    [159]Covell DG, Wallqvist A, Rabow AA, et al. Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data. Mol Cancer Ther. 2003,2(3):317-32.
    [160]Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci USA, 2000,97(22): 12079-84.
    [161]Hastie T, Tibshirani R, Eisen MB, et al. 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol, 2000,1(2):RESEARCH0003.
    [162]Jiang H, Deng Y, Chen HS, et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics, 2004,5(1 ):81.
    [163]Lazzeroni L, Owen A. Plaid models for gene expression data. Statistica Sinica 2002, 12:61-86.
    [164]Plaid models, for microarrays and DNA expression [http://www-stat.stanford.edu/~owen/plaid/]
    [165]Wang J, Delabie J, Aasheim H, et al. Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics, 2002, 3(1):36.
    [166]Herrero J, Dopazo J. Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns. J Proteome Res, 2002,1(5):467-70.
    [167]Billings SA, Lee KL. Nonlinear fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm. Neural Netw, 2002,15(2):263-70.
    [168]Cho JH, Lee D, Park JH, et al. Gene selection and classification from microarray data using kernel machine. FEBS Lett, 2004,571(1-3):93-8.
    [169]Dangond F, Hwang D, Camelo S, et al. Molecular signature of late-stage human ALS {evealed by expression profiling of postmortem spinal cord gray matter. Physiol Genomics, 2004,16(2): 229-39.
    [170]Friedman N, Linial M, Nachman I, et al. Using Bayesian networks to analyze expression data. J Comput Biol, 2000,7(3-4):601-20.
    [171]Imoto S, Higuchi T, Goto T, et al. Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. J Bioinform Comput Biol, 2004,2(1):77-98. .
    [172]Kim SY, Imoto S, Miyano S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform, 2003,4(3):228-35.
    [173]Pavlidis P, Weston J, Cai J, et al. Learning gene functional classifications from multiple data types. J Comput Biol, 2002,9(2):401-11.
    [174] Williams RD, Hing SN, Greer BT, et al. Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines. Genes Chromosomes Cancer, 2004,41(1):65-79.
    [175]Podgorelec V, Kokol P, Stiglic B, et al. Decision trees: an overview and their use in medicine. J Med Syst, 2002,26(5):445-63.
    [176]Dettling M, Buhlmann P. Boosting for tumor classification with gene expression data. Bioinformatics, 2003,19(9):1061-9.
    [177]Middendorf M, Kundaje A, Wiggins C, et al. Predicting genetic regulatory response using classification. Bioinformatics, 2004,20 Suppl 1:1232-1240.
    [178]Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal, 2000,22(5):717-27.
    [179]O'Neill MC, Song L. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect. BMC Bioinformatics, 2003,4(1):13.
    [180]Sawa T, Ohno-Machado L. A neural network-based similarity index for clustering DNA microarray data. Comput Biol Med, 2003,33(1): 1-15.
    [181]Liu A, Zhang Y, Gehan E, et al. Block principal component analysis with application to gene microarray data classification. Stat Med, 2002,21(22):3465-74.
    [182]Crescenzi M, Giuliani A. The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data. FEBS Lett, 2001,507(1):114-8.
    [183]Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics, 2001,17(9):763-74.
    [184]Slonim DK. From patterns to pathways: gene expression data analysis comes of age. Nat Genet, 2002,32 Suppl:502-8.
    [185]Hudson ME, Quail PH. Identification of promoter motifs involved in the network of phytochrome A-regulated gene expression by combined analysis of genomic sequence and microarray data. Plant Physiol, 2003,133(4):1605-16.
    [186]Gutierrez-Rios RM, Rosenblueth DA, Loza JA, et al. Regulatory network of Escherichia coli: consistency between literature knowledge and microarray profiles. Genome Res, 2003,13(11):2435-43.
    [187]Kellam P. Microarray gene expression database: progress towards an international repository of gene expression data. Genome Biol, 2001,2(5):REPORTS4011.
    [188]Killion PJ, Sherlock G, Iyer VR. The Longhorn Array Database (LAD): an open-source, MIAME compliant implementation of the Stanford Microarray Database (SMD). BMC Bioinformatics, 2003,4(1):32.
    [189]Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet, 2001,29(4):365-71.
    [190]Nguyen DV, Rocke DM. Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics, 2002,18(12):1625-32.
    [191]Nguyen DV, Rocke DM. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics, 2002,18(l):39-50.
    [192]Dembele D, Kastner P. Fuzzy C-means method for clustering microarray data. Bioinformatics, 2003,19(8):973-80.
    [193]Venet D. MatArray: a Matlab toolbox for microarray data. Bioinformatics, 2003,19(5):659-60.
    [194]Wang J, Myklebost O, Hovig E. MGraph: graphical models for microarray data analysis. Bioinformatics, 2003,19(17):2210-1.
    [195] http://www.insightful.com/products/splus/default.asp
    [196]De Smet F, Moreau Y, Engelen K, et al. Balancing false positives and false negatives for the detection of differential expression in malignancies. Br J Cancer, 2004,91 (6): 1160-5.
    [197]Walsh DP, Chang YT. Recent advances in small molecule microarrays: applications and technology. Comb Chem High Throughput Screen, 2004,7(6):557-64.
    [198] http://www.r-project.org
    [ 199]http://genopole.toulouse.inra.fr/%7Elucas/amap
    [200]http://cran.r-project.org/src/contrib/PACKAGES.html#cluster
    [201]http://cran.r-project.org/src/contrib/PACKAGES.html#cclust
    [202]http://cran.r-project.org/src/contrib/PACKAGES.html#mclust
    [203]http://cran.r-project.org/src/contrib/PACKAGES.html#e 1071
    [204]Saeed AI, Sharov V, White J, et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques, 2003,34(2):374-8.
    [205]Herrero J, Diaz-Uriarte R, Dopazo J. Gene expression data preprocessing. Bioinformatics, 2003,19(5):655-6.
    [206]Dudoit S, Gentleman RC, Quackenbush J. Open source software for the analysis of microarray data. Biotechniques, 2003,Suppl:45-51.
    [207]Boyle J. SeqExpress: desktop analysis and visualization tool for gene expression experiments. Bioinformatics, 2004,20(10): 1649-50.
    [208]Wettenhall JM, Smyth GK. limmaGUI: a graphical user interface for linear modeling of microarray data. Bioinformatics, 2004,20(18):3705-6.
    [209]Newton MA, Kendziorski CM, Richmond CS, et al. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J Comput Biol, 2001,8(1):37-52.
    [210]Vaquerizas JM, Dopazo J, Diaz-Uriarte R. DNMAD: web-based diagnosis and normalization for microarray data. Bioinformatics, 2004,20(18):3656-8.
    [211]Volinia S, Evangelisti R, Francioso F, et al. GOAL: automated Gene Ontology analysis of expression profiles. Nucleic Acids Res, 2004,32(Web Server issue): W492-9.
    [212]Herrero J, Vaquerizas JM, Al-Shahrour F, et al. New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Res, 2004,32(Web Server issue): W485-91.
    [213]Kapushesky M, Kemmeren P, Culhane AC, et al. Expression Profiler: next generation—an online platform for analysis of microarray data. Nucleic Acids Res, 2004,32(Web Server issue): W465-70.
    [214]Fink JL, Drewes S, Patel H, et al. 2HAPI: a microarray data analysis system. Bioinformatics, 2003,19(11):1443-5.
    [215]http://www.applied-maths.com/genemaths/genemaths.htm
    [216]http://www.predictivepatterns.com/docsAVebSiteDocs/Introduction/Front_Page. htm
    [217]Xia X, Xie Z. AMADA: analysis of microarray data. Bioinformatics, 2001,17(6):569-70.
    [218]http://ep.ebi.ac.uk/EP/
    [219]http://www.silicongenetics.com/cgi/SiG.cgi/Products/GeneSpring/index.smf
    [220]Sirava M, Schafer T, Eiglsperger M, et al. BioMiner-modeling, analyzing, and visualizing biochemical pathways and networks. Bioinformatics, 2002,18 Suppl 2:S219-S230.
    [221 ]Dahlquist KD, Salomonis N, Vranizan K, et al. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet, 2002,31(1): 19-20.
    [222]de Jong H, Geiselmann J, Hernandez C, et al. Genetic Network Analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics, 2003,19(3):336-44.
    [223]Chuaqui RF, Bonner RF, Best CJ, et al. Post-analysis follow-up and validation of microarray experiments. Nat Genet, 2002,32 Suppl:509-14.
    [224] Mimmack ML, Brooking J, Bahn S. Quantitative polymerase chain reaction: validation of microarray results from postmortem brain studies. Biol Psychiatry, 2004,55(4):337-45.
    [225] Lindvall JM, Blomberg KE, Berglof A, et al. Gene expression profile of B cells from Xid mice and Btk knockout mice. Eur J Immunol, 2004,34(7): 1981-91.
    [226] Wilson KS, Roberts H, Leek R, et al. Differential gene expression patterns in HER2/neu-positive and-negative breast cancer cell lines and tissues. Am J Pathol, 2002,161 (4): 1171-85.
    [227] Kao LC, Tulac S, Lobo S, et al. Global gene profiling in human endometrium during the window of implantation. Endocrinology, 2002,143(6):2119-38.
    [228] Srinivasan V, Pamula VK, Fair RB. An integrated digital microfluidic lab-on-a-chip for clinical diagnostics on human physiological fluids. Lab Chip, 2004,4(4):310-5.
    [229] Smyth GK, Michaud J, Scott HS. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics, 2005.
    [230] Wooster R. Cancer classification with DNA microarrays is less more?. Trends Genet, 2000, 16(8):327-9.
    [231] Draghici S, Khatri P, Bhavsar P, et al. Onto-Tools, the toolkit of the modem biologist: Onto-Express, Otto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res, 2003,31(13): 3775-81.
    [232] Costa Lda F. Bioinformatics: perspectives for the future. Genet Mol Res, 2004,3(4):564-74.
    [233] Singh GB, Singh H. Databases, models, and algorithms for functional genomics: a bioinformatics perspective. Mol Biotechnol, 2005,29(2): 165-84.
    [234] McKusick, V.A.: Mendelian Inheritance in Man. A Catalog of Human Genes and Genetic Disorders. Baltimore: Johns Hopkins University Press, 1998 (12th edition).
    [235] Cantor MN, Lussier YA. Mining OMIM for Insight into Complex Diseases. Medinfo, 2004,2004:753-7.
    [236] Hamcsh A, Scott AF, Amberger JS, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res, 2005,33 Database Issue:D514-7.
    [237] Boyadjiev SA, Jabs EW. Online Mendelian Inheritance in Man (OMIM) as a knowledgebase for human developmental disorders. Clin Genet, 2000,57(4):253-66.
    [238] 林乔,吴斌,王米渠,等.现代生物学科中的阴阳属性与阴阳变易.现代中西医结合杂志,2004,13(4):421-2
    [239] 林乔,吴斌,王米渠.中医阴阳系统病理学探索:中药与基因.现代中西医结合杂志,2004,13(18):1-4
    [240] 林乔,王米渠,吴斌,等.中医理论与人类基因组的研究.中医药学刊,2003,9,21(8)1232-6
    [241] 林乔,吴斌,王米渠,等.头痛的相关基因及《名医类案》中几个病例的基因分析.上海中医药大学学报,2003,17(3)42-5
    [242] 林乔,王米渠,吴斌,寒热辨证与基因.中华现代临床医药杂志,2002,3(11):34-9
    [243] 林乔,王米渠.人类基因的定势表达与疾病.美国中华健康:卫生杂志,2002,5(8):69-77
    [244] 林乔,王米渠.《宋史》人物遗传疾病和环境对寿限的影响.遗传学报,2000,27(12):1049-56
    [245] 林乔,王米渠.辨病证的虚实与基因的虚实.《中国现代医药与临床》,北京,中国科学技术出版社,2002:107-17
    [246] Hood L. A personal view of molecular technology and how it has changed biology. J Proteome Res, 2002,1(5):399-409.
    [247] 王米渠,吴斌,严石林,等.论虚寒证基因芯片及生物信息的高起点切入研究.辽宁中医杂志,2003,30(3):166-71
    [248] 王米渠,吴斌,严石林,等.从分子生物学的角度探讨中医藏象学说的内涵.广州中医药大学学报.2002,19(4):314-5
    [249] 王米渠,严石林,吴斌,等.虚寒证辩证因子等级量化标准的研充.辽宁中医杂志,2003,30(4):249-50
    [250] 严石林,李炜弘,王米渠.寒证辨证因子等级量化操作标准的研究.中国中医药信息杂志,2002,9(8):64-6
    [251] 严石林,高峰,吴斌,等.肾阳虚证半定量化操作标准的研究.现代中西医结合杂志.2003.13(6):701-2
    [252] 严石林,张连文,王米渠,等.肾虚证辨证因子等级评判操作标准的研究.成都中医药大学学报,2001,24(1):56-9
    [253] 吴斌,王米渠,严石林,等.典型虚寒证的辨证统计分析.现代中西医结合杂志.2003,13(5):561-2
    [254] http://gepas.bioinfo.cnio.es/cgi-bin/preprocess
    [255] http://genome.tugraz.at/
    [256] Al-Shahrour F, Diaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 2004,20(4):578-80.
    [257] Rosenberg NA, Pritchard JK, Weber JL, et al. Genetic structure of human populations. Science, 2002,298(5602):2381-5.
    [258] Rannala B. Finding genes influencing susceptibility to complex diseases in the post-genome era. Am J Pharmacogenomics, 2001,1(3):203-21.
    [259] Vawter MP, Ferran E, Galke B, et al. Microarray screening of lymphocyte gene expression differences in a multiplex schizophrenia pedigree. Schizophr Res, 2004,67(1):41-52.
    [260] 吴斌,高峰,严石林,等.肾阳虚证的辨证因子规律初探.现代中西医结合杂志,2004,13(14):1819-20
    [261] Andersen MT, Foy CA. The development of microarray standards. Anal Bioanal Chem, 2005,381(1):87-9.
    [262] Ball C, Brazma A, Causton H, et al. Standards for microarray data: an open letter. Environ Health Perspect, 2004,112(12):A666-7.
    [263] 王米渠,张敬远,丁维俊,等.寒证基因芯片数据库的聚类分析方法研究.中国中医基础医学杂志,2002,8(12):62-4
    [264] Herrero J, Al-Shahrour F, Diaz-Uriarte R, et al. GEPAS: A web-based resource for microarray gene expression data analysis. Nucleic Acids Res, 2003,31 (13):3461-7.
    [265] Keselman HJ, Cribbie R, Holland B. Controlling the rate of Type I error over a large set of statistical tests. Br J Math Star Psychol, 2002,55(Pt 1):27-39.
    [266] Churchill GA. Using ANOVA to analyze microarray data. Biotechniques, 2004,37(2):173-5, 177.
    [267] Lewis SE. Gene Ontology: looking backwards and forwards. Genome Biol, 2005,6(1):103.
    [268] Li S, Becich MJ, Gilbertson J. Microarray data mining using gene ontology. Medinfo, 2004, 2004:778-82.
    [269] Smith B, Williams J, Schulze-Kremer S. The ontology of the gene ontology. AMIA Annu Syrup Proc, 2003:609-13.
    [270] Takai T, Takagi T. [Introduction to gene ontology]. Tanpakushitsu Kakusan Koso, 2003,48(1): 79-85.
    [271] Lercher MJ, Urrutia AO, Hurst LD. Clustering of housekeeping genes provides a unified model of gene order in the human genome. Nat Genet, 2002,31 (2): 180-3.
    [272] Doniger SW, Salomonis N, Dahlquist Kd, et al. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol, 2003, 4(1):R7.
    [273] Kanehisa M, Goto S, Kawashima S, et al. The KEGG resource for deciphering the genome. Nucleic Acids Res, 2004,32(Database issue):D277-80.
    [274] http://www.biocarta.com/
    [275] 吴斌,林乔,王米渠.中医遗传学与个体遗传系统.中医杂志,2004,45(3):167-9
    [276] Yang ZR. Biological applications of support vector machines. Brief Bioinform, 2004,5(4):328-38.
    [277] Raudys S, How good are support vector machines?. Neural Netw, 2000,13(1): 17-9.
    [278] Pontil M, Verri A. Properties of support vector machines. Neural Comput, 1998,10(4):955-74.
    [279] Quaglino P, Savoia P, Osella-Abate S,et al.RT-PCR tyrosinase expression in the peripheral blood of melanoma patients.Expert Rev Mol Diagn.2004,4(5):727-41
    [280] Ombandza-Moussa E, Dussaix E, Roque-Afonso AM. Hepatitis delta virus RNA detection by one-step RT-PCR.Ann Biol Clin (Paris).2004,62(3):319-24.
    [281] Tyagi S, Kramer FR. Molecular beacons: probes that fluoresce upon hybridization.Nat Biotechnol. 1996,14(3):303-8
    [282] Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays.J Mol Endocrinol.2000,25(2): 169-93.
    [283] Guiver M, Borrow R, Marsh J, et al. Evaluation of the Applied Biosystems automated Taqman polymerase chain reaction system for the detection of meningococcal DNA.FEMS Immunol Med Microbiol.2000,28(2): 173-9.
    [284] Morrison TB, Weis JJ, Wittwer CT. Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification.Biotechniques. 1998,24(6):954-8, 960, 962
    [285] Simpson DA, Feeney S, Boyle C,et al. Retinal VEGF rnRNA measured by SYBR green I fluorescence: A versatile approach to quantitative PCR.Mol Vis. 2000,6:178-83.
    [286] Xia AP, Kikuchi T, Minowa O, et al. Late-onset hearing loss in a mouse model of DFN3 non-syndromic deafness: morphologic and immunohistochemical analyses.Hear Res. 2002,166(1-2): 150-8.
    [287] Douville PJ, Atanasoski S, Tobler A, et al. The brain-specific POU-box gene Brn4 is a sex-linked transcription factor located on the human and mouse X chromosomes.Mamm Genome. 1994,5(3):180-2.
    [288] de Kok YJ. van der Maarel SM, Bitner-Glindzicz M, et al. Association between X-linked mixed deafness and mutations in the POU domain gene POU3F4.Science. 1995,267(5198):685-8:
    [289] 吴斌,严石林,王米渠.浅谈系统性红斑狼疮的基本病机.现代中西医结合杂志.2004,13(12):1539-40
    [290] 严石林,吴斌,高峰,等.肾阳虚“寒火”证的辨证规律研究.现代中西医结合杂志,2004,13(19):2519-20
    [291] 高泓,吴斌,王米渠,等.健脾益肾化痰逐瘀法治疗糖尿病合并脂代谢紊乱.英国中医药学会会刊,2003(24):1-2
    [292] 王米渠,吴斌,张卫,等.补肾灵对慢性肾衰模型大鼠生化指标的影响.上海中医药大学学报,2002,16(3):37-9

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700