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

doi:10.1007/s00439-020-02253-0

	A network-based machine-learning framework to identify both functional modules and disease genes
	Yang, Kuo 1,2; Lu, Kezhi 1,7; Wu, Yang 3; Yu, Jian 4; Liu, Baoyan 5; Zhao, Yi 3; Chen, Jianxin 6; Zhou, Xuezhong 1,5
	2021-01-07
发表期刊	HUMAN GENETICS
ISSN	0340-6717
页码	17
摘要	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.
DOI	10.1007/s00439-020-02253-0
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2017YFC1703506] ; National Key Research and Development Program[2017YFC1703502] ; National Science and Technology Major Project[2019ZX09201005-002-006] ; National Key R&D Program of China[2018AAA0100302] ; Fundamental Research Funds for the Central Universities[2018JBZ006] ; Fundamental Research Funds for the Central Universities[ZZ10-005] ; Special Programs of Traditional Chinese Medicine[JDZX2015170] ; Special Programs of Traditional Chinese Medicine[JDZX2015171]
WOS研究方向	Genetics & Heredity
WOS类目	Genetics & Heredity
WOS记录号	WOS:000605523000001
出版者	SPRINGER
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16346
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Xuezhong
作者单位	1.Beijing Jiaotong Univ, Inst Med Intelligence, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 2.Tsinghua Univ, Dept Automat, MOE Key Lab Bioinformat, Inst TCM X,Bioinformat Div,BNRIST, Beijing 10084, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Adv Comp Res Ctr, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing Key Lab Traff Data Anal & Min, Beijing 100044, Peoples R China 5.China Acad Chinese Med Sci, Data Ctr Tradit Chinese Med, Beijing 100700, Peoples R China 6.Beijing Univ Chinese Med, Beijing 100029, Peoples R China 7.Katholieke Univ Leuven, Imec DistriNet, B-3001 Leuven, Belgium
推荐引用方式 GB/T 7714	Yang, Kuo,Lu, Kezhi,Wu, Yang,et al. A network-based machine-learning framework to identify both functional modules and disease genes[J]. HUMAN GENETICS,2021:17.
APA	Yang, Kuo.,Lu, Kezhi.,Wu, Yang.,Yu, Jian.,Liu, Baoyan.,...&Zhou, Xuezhong.(2021).A network-based machine-learning framework to identify both functional modules and disease genes.HUMAN GENETICS,17.
MLA	Yang, Kuo,et al."A network-based machine-learning framework to identify both functional modules and disease genes".HUMAN GENETICS (2021):17.