期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：4
页码：3682-3687
出版社：TechScience Publications
摘要：With more and more biological information generated, the most pressing task of bioinformatics has become to analyze and interpret various types of data, including nucleotide and amino acid sequences, protein structures, gene expression profiling and so on. We apply the data mining techniques of feature generation, feature selection, and feature integration with learning algorithms to tackle the problems of disease phenotype classification and patient survival prediction from gene expression profiles, and the problems of functional site prediction from DNA sequences. When dealing with problems arising from gene expression profiles, we propose a new feature selection process for identifying genes associated with disease phenotype classification or patient survival prediction. This method, GSA and GFA algorithms aims to select a set of sharply discriminating genes with little redundancy by combining entropy measure, Wilcoxon rank sum test and Pearson correlation coefficient test. In the study of patient survival prediction, we present a new idea of selecting informative training samples by defining long-term and short-term survivors. GFA is then applied to identify genes from these samples. A regression function built on the selected samples and genes by a linear kernel SVM is worked out to assign a risk score to each patient. In order to apply data mining methodology to identify functional sites in biological sequences, we first generate candidate features using k k-gram nucleotide acid or amino acid patterns and then transform original sequences respect to the new constructed feature space.
