한빛사 논문
Chan Yeong Kim1,†, Seungbyn Baek1,†, Junha Cha1, Sunmo Yang1, Eiru Kim2, Edward M. Marcotte3,4, Traver Hart2 and Insuk Lee1,*
1Department of Biotechnology, College of Life Sciences and Biotechnology, Yonsei University, Seoul 03722, Korea, 2Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA, 3Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas, Austin, TX 78712, USA and 4Department of Molecular Biosciences, University of Texas at Austin, TX 78712, USA
*To whom correspondence should be addressed.
†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
Abstract
Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.
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