【摘 要】
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In this paper, we firstly identify the functional modules enriched with differentially expressed genes (DEGs) and characterized by biological processes in speci
【机 构】
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School of Life Science and Bioinformatics Centre,Department of Bioinformatics
论文部分内容阅读
In this paper, we firstly identify the functional modules enriched with differentially expressed genes (DEGs) and characterized by biological processes in specific cellular locations, based on gene ontology (GO) and microarray data. Then, we further define and filter disease relevant signature modules according to the ranking of the disease discriminating abilities of the pre-selected functional modules. At last, we analyze the potential way by which they cooperate towards human disease. Application of the proposed method to the analysis of a liver cancer dataset shows that, using the same false discovery rate (FDR) threshold, we can find more biologically meaningful and detailed processes by using the cellular localization information. Some biological evidences support the relevancy of our biological modules to the disease mechanism.
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