HumanNet v2: Human gene networks for disease research

Sohyun Hwang, Chan Yeong Kim, Sunmo Yang, Eiru Kim, Traver Hart, Edward M. Marcotte, Insuk Lee

Research output: Contribution to journalArticle

17 Citations (Scopus)


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 (, 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.

Original languageEnglish
Pages (from-to)D573-D580
JournalNucleic acids research
Issue numberD1
Publication statusPublished - 2019 Jan 8

All Science Journal Classification (ASJC) codes

  • Genetics

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    Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M., & Lee, I. (2019). HumanNet v2: Human gene networks for disease research. Nucleic acids research, 47(D1), D573-D580.