title: A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.
abstract: Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.
authors.forename: Tzu-Hao
authors.initials: TH
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan 333, Taiwan.
authors.forename: Shih-Lin
authors.initials: SL
authors.lastname: Wu
authors.affiliation: Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan.
authors.forename: Wei-Jen
authors.initials: WJ
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan ; Department of Biomedical Informatics, Asia University, Taichung 413, Taiwan.
authors.forename: Jorng-Tzong
authors.initials: JT
authors.lastname: Horng
authors.affiliation: Department of Information Management, Hsing Wu University, New Taipei City 244, Taiwan.
authors.forename: Cheng-Wei
authors.initials: CW
authors.lastname: Chang
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software