HumanNet v3: An improved database of human gene networks for disease research

Chan Yeong Kim, Seungbyn Baek, Junha Cha, Sunmo Yang, Eiru Kim, Edward M. Marcotte, Traver Hart, Insuk Lee

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


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 (, 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.

Original languageEnglish
Pages (from-to)D632-D639
JournalNucleic acids research
Issue numberD1
Publication statusPublished - 2022 Jan 7

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.

All Science Journal Classification (ASJC) codes

  • Genetics


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