4.7 Article

Network-based prioritization of cancer genes by integrative ranks from multi-omics data

期刊

COMPUTERS IN BIOLOGY AND MEDICINE
卷 119, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2020.103692

关键词

Cancer gene prioritization; Multi-omics data integration; Multiplex networks; Constrained PageRank

资金

  1. National Natural Science Foundation of China (NSFC) [61973190, 61572287, 61533011]
  2. Key Research and Development Project of Shandong Province, China [2018GSF118043]
  3. Innovation Method Fund of China (Ministry of Science and Technology of China) [2018IM020200]
  4. Program of Qilu Young Scholars of Shandong University

向作者/读者索取更多资源

Finding disease genes related to cancer is of great importance for diagnosis and treatment. With the development of high-throughput technologies, more and more multiple-level omics data have become available. Thus, it is urgent to develop computational methods to identify cancer genes by integrating these data. We propose an integrative rank-based method called iRank to prioritize cancer genes by integrating multi-omics data in a unified network-based framework. The method was used to identify the disease genes of hepatocellular carcinoma (HCC) in humans using the multi-omics data for HCC from TCGA after building up integrated networks in the corresponding molecular levels. The kernel of iRank is based on an improved PageRank algorithm with constraints. To demonstrate the validity and the effectiveness of the method, we performed experiments for comparison between single-level omics data and multiple omics data as well as with other algorithms: random walk (RW), random walk with restart on heterogeneous network (RWH), PRINCE and PhenoRank. We also performed a case study on another cancer, prostate adenocarcinoma (PRAD). The results indicate the effectiveness and efficiency of iRank which demonstrates the significance of integrating multi-omics data and multiplex networks in cancer gene prioritization.

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