Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms- protein-protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.
Bibliographical noteFunding Information:
National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) [NRF- 2018M3C9A5064709, NRF-2018R1A5A2025079 to I.L., NRF-2018R1C1B5032617 to S.H.]; Brain Korea 21 (BK21) PLUS Program (to I.L.); NIH (to E.M.M.); NSF (to E.M.M.); Welch Foundation (F-1515) (to E.M.M.); CPRIT Grant [RR160032 to E.K., T.H.]; NIH Grants [R35GM130119, P30 CA016672 to T.H.]. Funding for open access charge: National Research Foundation of Korea.
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