期刊
NUCLEIC ACIDS RESEARCH
卷 47, 期 D1, 页码 D573-D580出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gky1126
关键词
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资金
- National Research Foundation of Korea (NRF) - Korean Government (MSIT) [NRF-2018M3C9A5064709, NRF-2018R1A5A2025079, NRF-2018R1C1B5032617]
- Brain Korea 21 (BK21) PLUS Program
- NIH [R35GM130119, P30 CA016672]
- NSF
- Welch Foundation [F-1515]
- CPRIT Grant [RR160032]
- National Research Foundation of Korea
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.
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