期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 16, 期 3, 页码 782-791出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2018.2866836
关键词
Biweight midcorrelation; differential correlation; nonconvex penalty; gene regulatory network; stability selection
类别
资金
- National Natural Science Foundation of China [61732012, 61520106006, 31571364, 61532008, U1611265, 61672382, 61772370, 61702371, 61772357, 61672203]
- China Postdoctoral Science Foundation [2017M611619, 2016M601646]
- BAGUI Scholar Program of Guangxi Zhuang Autonomous Region of China
Underlying a cancer phenotype is a specific gene regulatory network that represents the complex regulatory relationships between genes. It remains, however, a challenge to find cancer-related gene regulatory network because of insufficient sample sizes and complex regulatory mechanisms in which gene is influenced by not only other genes but also other biological factors. With the development of high-throughput technologies and the unprecedented wealth of multi-omics data it gives us a new opportunity to design machine learning method to investigate underlying gene regulatory network. In this paper, we propose an approach, which use Biweight Midcorrelation to measure the correlation between factors and make use of Nonconvex Penalty based sparse regression for Gene Regulatory Network inference (BMNPGRN). BMNCGRN incorporates multi-omics data (including DNA methylation and copy number variation) and their interactions in gene regulatory network model. The experimental results on synthetic datasets show that BMNPGRN outperforms popular and state-of-the-art methods (inducing DCGRN, ARACNE, and CLR) under false positive control. Furthermore, we applied BMNPGRN on breast cancer (BRCA) data from The Cancer Genome Atlas database and provided gene regulatory network.
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