【摘 要】
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Background: To detect the most promising genes from the large list of candidate genes is defined as gene prioritization problem.Prioritization of cancer-associated genes would facilitate us to underst
【机 构】
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MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automatio
【出 处】
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第五届全国生物信息学与系统生物学学术大会
论文部分内容阅读
Background: To detect the most promising genes from the large list of candidate genes is defined as gene prioritization problem.Prioritization of cancer-associated genes would facilitate us to understand the mechanism of cancer development and provide us promising candidates of drug target.Methods: In this paper, we furthered our recent work and developed a method, Networked Gene Prioritizer 2.0 (NGP2), to prioritize cancer-associated genes.The existing network based methods (e.g., heat kernel ranking method of PINTA) and the method based on differential expression analysis (DE analysis) prioritize genes by their difference between compared samples.However, NGP2 integrates network analysis and enrichment analysis to infer genes functional status in compared samples.It prioritizes genes in case data and control data respectively.Results: We applied NGP2, heat kernel ranking method of PINTA (HKR) and DE analysis on several breast cancer and lung cancer datasets.David functional annotation results showed NGP2 performs better than HKR and DE analysis.The enrichment results of topranking genes of HKR and DE analysis showed the pathways that are different between case and control samples while in NGP2 the enrichment results of top-ranking genes suggested the pathways that are activated in case samples and control samples respectively.In addition, compared with HKR and DE analysis, the top-ranking genes of NGP2 tend to be enriched in cancer-associated pathways.On the lung cancer datasets, we demonstrated that cell cycle related functional terms in Gene Ontology (e.g., "cell cycle") are activated in smoking lung cancer samples, non-smoking lung cancer samples but not in normal samples.Conclusions: In this paper, we have developed a method named NGP2, to prioritize cancer-associated genes.Our results demonstrated NGP2 performs better than the existing methods .
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