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Mining gene expression data using a novel approach based on hidden Markov models
Author(s) -
Ji Xinglai,
Li-Ling Jesse,
Sun Zhirong
Publication year - 2003
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(03)00363-6
Subject(s) - cluster analysis , computer science , data mining , hidden markov model , markov chain , expression (computer science) , markov model , microarray analysis techniques , construct (python library) , gene regulatory network , computational biology , gene , artificial intelligence , gene expression , machine learning , biology , genetics , programming language
In this work we have developed a new framework for microarray gene expression data analysis. This framework is based on hidden Markov models. We have benchmarked the performance of this probability model-based clustering algorithm on several gene expression datasets for which external evaluation criteria were available. The results showed that this approach could produce clusters of quality comparable to two prevalent clustering algorithms, but with the major advantage of determining the number of clusters. We have also applied this algorithm to analyze published data of yeast cell cycle gene expression and found it able to successfully dig out biologically meaningful gene groups. In addition, this algorithm can also find correlation between different functional groups and distinguish between function genes and regulation genes, which is helpful to construct a network describing particular biological associations. Currently, this method is limited to time series data. Supplementary materials are available at http://www.bioinfo.tsinghua.edu.cn/~rich/hmmgep_supp/.