A network-based machine-learning framework to identify both functional modules and disease genes.

Journal: Human genetics
PMID: 33409574

Abstract

Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.

Authors

  • Kuo Yang
  • Kezhi Lu
    School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China.
  • Yang Wu
  • Jian Yu
    Key laboratory of Transplantation, Chinese Academy of Medical Sciences, Tianjin, 300192, China; Tianjin Key Laboratory for Organ Transplantation, Tianjin First Center Hospital, Tianjin, 300192, China; Department of Liver Transplantation, Tianjin Medical University First Center Clinical College, Tianjin, 300192, China; Tianjin Key Laboratory of Molecular and Treatment of Liver Cancer, Tianjin First Center Hospital, Tianjin, 300192, China.
  • Baoyan Liu
    China Academy of Chinese Medical Sciences.
  • Yi Zhao
    Department of Biostatistics and Health Data Science, Indiana University School of Medicine.
  • Jianxin Chen
    Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address: cjx@bucm.edu.cn.
  • Xuezhong Zhou
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

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