HumanNet v2: human gene networks for disease research
Open accessAbstract
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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Gene interactions usually exist in the form of networks, such as gene regulatory networks and biological signaling pathways.
“Second, gene interactions usually exist in the form of networks (i.e., gene regulatory networks and biological signaling pathways) 43 , and this kind of prior knowledge hasn't been explicitly incorporated in scBERT.”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [43]
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- Authors
- Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M., & Lee, I.
- Affiliations
- Yonsei University (KR)
- The University of Texas MD Anderson Cancer Center (US)
- The University of Texas at Austin (US)
- Corresponding author
- Lee, Insuk
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-10
- Volume
- 47
- Issue
- D1
- Pages
- D573-D580
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6323914
- OpenAlex
- W2899728356
- Cited by
- 210
- References
- 48
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 25 · 2023: 31 · 2022: 44 · 2021: 45 · 2020: 31 · 2019: 15 · 2018: 2
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF-2018M3C9A5064709, NRF-2018R1A5A2025079, NRF-2018R1C1B5032617)
- Welch Foundation (F-1515)
- Cancer Prevention and Research Institute of Texas (RR160032)
- National Institutes of Health (R35GM130119, P30 CA016672)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- disease gene prediction, network medicine, gene-disease association, network inference algorithms, multi-omics integration
- MeSH terms
- Algorithms, Disease, Humans, User-Computer Interface, Databases, Genetic, Gene Regulatory Networks
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