生物信息数据挖掘中的若干方法及其应用研究
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摘要
许多生物(包括人在内)的基因组测序已经完成或接近完成,在揭示这些巨量数据所蕴涵的信息时,产生了一门新的交叉学科—生物信息学,通过对生物学实验数据的获取、加工、存储、检索与分析,进而达到揭示数据所蕴含的生物学意义的目的。数据挖掘技术用于在数据库中发现潜在有用的知识,在生物信息学研究当中,正发挥着越来越重要的作用,而且取得了丰硕的成果。本文探讨若干生物信息数据挖掘的方法及其应用,主要工作如下:
1.用支持向量机和FDOD两种方法对同源寡聚蛋白质进行了分类研究。Garian R.利用决策树方法从蛋白质一级结构出发对同源二聚体和同源非二聚体进行了分类,证实了蛋白质一级结构即氨基酸序列包含四级结构信息。本文用SVM和FDOD两种方法对同源二聚体和同源非二聚体进行分类,利用原始序列的子序列分布作为特征向量。采用和决策树方法同样的数据集,两种方法均大幅度提高了预测准确率。本文也对同源二聚体、同源三聚体、同源四聚体和同源六聚体进行了分类,取得了好的结果。
2.构造了基于线性规划的ν-SVM分类器。Scholkopf B等提出的基于二次规划的ν-支持向量机(ν-SVM)相比标准的SVM,其优势在于可以控制支持向量的数目和误差,但由于增加了模型的复杂性,限制了其应用。本文构造了一种基于线性规划的ν-SVM分类器,模型简单,参数ν具有明确的意义,同样可以控制支持向量的数目和误差,可以直接利用比较成熟的线性规划算法。数值试验表明,本文提出的基于线性规划的ν-SVM的训练速度要比基于二次规划的ν-SVM快得多,而分类效果两者相当。
3.提出了无参数鲁棒线性规划支持向量机分类的牛顿算法。Mangasarian O L最近提出的无参数鲁棒线性规划支持向量机克服了标准SVM需要选取正则化参数等一些缺点,其模型是一个线性规划。本文给出了这种线性规划的精确的最小2-范数解,在此基础上提出了快速的牛顿算法,此算法只需要一个线性方程组解算器。理论、数值实验以及在癌症基因表达数据分类上的应用都表明了用牛顿算法实现的无参数鲁棒线性规划支持向量机模型合理、简单,算法快速、容易实现。
4.用FDOD方法对DNA序列进行相似性分析。序列的比较是生物信息学中最常用的研究手段之一,其根本任务是发现序列之间的相似性和不相似性。序列比对是序列比较的主要方法,但有其不足之处,所以很多人寻求用其他方法来比较DNA序列。本文
The sequencing of several genomes, including the human genome, has provided a vast amount of data which must be exploited. Bioinformatics is essentially the science of taking this. In Bioioformatics researchers study how to capture, manage, deposit, retrieve, analyze biological information enabling the discovery of encyclopedic biological knowledge. Data mining technology is used to extract potential and useful information from the databases, and is playing an increasingly important role in the study of Bioioformatics and bear fertile fruits. This paper investigates some data mining methods for bioinformatics and their application. The main work is summarized as followings:1. Both support vector machine and FDOD methods are applied to classification of homo-oligomeric proteins. Garian R used decision tree method to discriminate between homodimers and non-homodimers from the primary structure and showed that protein primary sequence contains quaternary structure information. In this present work, support vector machine and FDOD methods are applied to discriminating between homodimers and non-homodimers, where for training and testing protein primary sequences, their subsequence distributions act as input vectors. The classification results of the two methods are much better than that of the previous method on the same data set. The two methods are also applied to discriminating between homodimers, homotrimers, homotetramers and homohexamers from the protein primary structure, and the results are also good.2. A new v - SVM classifier based on linear programming is proposed. The v - SVM Classifier proposed by Scholkopf B has the advantage of controlling numbers of support vectors and errors compared to regular SVM, However, Its formulation is more complicated, which confines its applications. We present a new and simpler v - SVM classifier based on linear programming, The parameter v also has implicit sense of controlling numbers of support vectors and errors. Furthermore we can use effective linear programming solvers available. Numerical tests show that our v - SVM based on linear programming is much faster than original v - SVM and performs comparably in accuracy.3. A Newton method for parameterless robust linear programming support vector machine is presented. Parameterless robust linear programming support vector machine for classification,
recently proposed by Mangasarian O L, solved this issue of determining the size of regular parameter. We have discussed the least 2 - norm solution of the parameterless linear programming problem and then presented a fast Newton method. The algorithm requires only a linear equation solver. The theory, numerical tests and application to gene expression data for cancer classification demonstrate that it is simple, fast and easily accessible.4. FDOD is applied to analysis of similarities of DNA sequences. Comparison of sequences is one of the most common study means in Bioinformatics. Comparison of sequences aims at analyzing the similarity and dissimilarity of DNA sequences. It mainly depends on sequence alignment, which has some shortcomings. So people try to develop new methods. FDOD is used to analyze the similarities of DNA sequences from the primary structure. The effect of residue order along the sequence is taken into account in some extent. For different length of subsequence the approach is illustrated through the examination of similarities among the coding sequences of the first exon of β-globin gene of 11 different species. The resultsdemonstrate that FDOD method is effective.5. A novel 2-D graphical representation of DNA sequences and corresponding numerical characterization approach are proposed, and then applied to examining the similarities of DNA sequences. Graphical representations of DNA sequences allow visual inspection of data, and can facilitate the analysis, comparison and identification of such sequences. This paper considers a novel 2-D graphical representation of DNA sequences according to homomorphism in Algebra and chemical structure classification of
1.用支持向量机和FDOD两种方法对同源寡聚蛋白质进行了分类研究。Garian R.利用决策树方法从蛋白质一级结构出发对同源二聚体和同源非二聚体进行了分类,证实了蛋白质一级结构即氨基酸序列包含四级结构信息。本文用SVM和FDOD两种方法对同源二聚体和同源非二聚体进行分类,利用原始序列的子序列分布作为特征向量。采用和决策树方法同样的数据集,两种方法均大幅度提高了预测准确率。本文也对同源二聚体、同源三聚体、同源四聚体和同源六聚体进行了分类,取得了好的结果。
2.构造了基于线性规划的ν-SVM分类器。Scholkopf B等提出的基于二次规划的ν-支持向量机(ν-SVM)相比标准的SVM,其优势在于可以控制支持向量的数目和误差,但由于增加了模型的复杂性,限制了其应用。本文构造了一种基于线性规划的ν-SVM分类器,模型简单,参数ν具有明确的意义,同样可以控制支持向量的数目和误差,可以直接利用比较成熟的线性规划算法。数值试验表明,本文提出的基于线性规划的ν-SVM的训练速度要比基于二次规划的ν-SVM快得多,而分类效果两者相当。
3.提出了无参数鲁棒线性规划支持向量机分类的牛顿算法。Mangasarian O L最近提出的无参数鲁棒线性规划支持向量机克服了标准SVM需要选取正则化参数等一些缺点,其模型是一个线性规划。本文给出了这种线性规划的精确的最小2-范数解,在此基础上提出了快速的牛顿算法,此算法只需要一个线性方程组解算器。理论、数值实验以及在癌症基因表达数据分类上的应用都表明了用牛顿算法实现的无参数鲁棒线性规划支持向量机模型合理、简单,算法快速、容易实现。
4.用FDOD方法对DNA序列进行相似性分析。序列的比较是生物信息学中最常用的研究手段之一,其根本任务是发现序列之间的相似性和不相似性。序列比对是序列比较的主要方法,但有其不足之处,所以很多人寻求用其他方法来比较DNA序列。本文
The sequencing of several genomes, including the human genome, has provided a vast amount of data which must be exploited. Bioinformatics is essentially the science of taking this. In Bioioformatics researchers study how to capture, manage, deposit, retrieve, analyze biological information enabling the discovery of encyclopedic biological knowledge. Data mining technology is used to extract potential and useful information from the databases, and is playing an increasingly important role in the study of Bioioformatics and bear fertile fruits. This paper investigates some data mining methods for bioinformatics and their application. The main work is summarized as followings:1. Both support vector machine and FDOD methods are applied to classification of homo-oligomeric proteins. Garian R used decision tree method to discriminate between homodimers and non-homodimers from the primary structure and showed that protein primary sequence contains quaternary structure information. In this present work, support vector machine and FDOD methods are applied to discriminating between homodimers and non-homodimers, where for training and testing protein primary sequences, their subsequence distributions act as input vectors. The classification results of the two methods are much better than that of the previous method on the same data set. The two methods are also applied to discriminating between homodimers, homotrimers, homotetramers and homohexamers from the protein primary structure, and the results are also good.2. A new v - SVM classifier based on linear programming is proposed. The v - SVM Classifier proposed by Scholkopf B has the advantage of controlling numbers of support vectors and errors compared to regular SVM, However, Its formulation is more complicated, which confines its applications. We present a new and simpler v - SVM classifier based on linear programming, The parameter v also has implicit sense of controlling numbers of support vectors and errors. Furthermore we can use effective linear programming solvers available. Numerical tests show that our v - SVM based on linear programming is much faster than original v - SVM and performs comparably in accuracy.3. A Newton method for parameterless robust linear programming support vector machine is presented. Parameterless robust linear programming support vector machine for classification,
recently proposed by Mangasarian O L, solved this issue of determining the size of regular parameter. We have discussed the least 2 - norm solution of the parameterless linear programming problem and then presented a fast Newton method. The algorithm requires only a linear equation solver. The theory, numerical tests and application to gene expression data for cancer classification demonstrate that it is simple, fast and easily accessible.4. FDOD is applied to analysis of similarities of DNA sequences. Comparison of sequences is one of the most common study means in Bioinformatics. Comparison of sequences aims at analyzing the similarity and dissimilarity of DNA sequences. It mainly depends on sequence alignment, which has some shortcomings. So people try to develop new methods. FDOD is used to analyze the similarities of DNA sequences from the primary structure. The effect of residue order along the sequence is taken into account in some extent. For different length of subsequence the approach is illustrated through the examination of similarities among the coding sequences of the first exon of β-globin gene of 11 different species. The resultsdemonstrate that FDOD method is effective.5. A novel 2-D graphical representation of DNA sequences and corresponding numerical characterization approach are proposed, and then applied to examining the similarities of DNA sequences. Graphical representations of DNA sequences allow visual inspection of data, and can facilitate the analysis, comparison and identification of such sequences. This paper considers a novel 2-D graphical representation of DNA sequences according to homomorphism in Algebra and chemical structure classification of
引文
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