HumanNet Edit

Citations: 520

z-index: 65

Basic information
Short name HumanNet
Full name human gene networks for disease research
Description a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms.
URL http://www.inetbio.org/humannet
Year founded 2011
Last update & version 2018 v2
Availability Free to academic users only
Contact information
University/Institution hosted Yonsei University
Address Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea.
City Seoul
Province/State
Country/Region Korea, Republic of
Contact name Insuk Lee
Contact email insuklee@yonsei.ac.kr
Data information
Data object
  • Animal
Data type
  • DNA
  • Protein
Database category
  • Interaction
Major organism
  • Homo sapiens
Keyword
  • network
  • human
Publications
  • HumanNet v2: human gene networks for disease research. [PMID: 30418591]
    Sohyun Hwang, Chan Yeong Kim, Sunmo Yang, Eiru Kim, Traver Hart, Edward M Marcotte, Insuk Lee

    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.

    Nucleic Acids Res. 2019:47(D1)

    0 Citations (from Europe PMC, 2019-01-19)

  • Prioritizing candidate disease genes by network-based boosting of genome-wide association data. [PMID: 21536720]
    Insuk Lee, U Martin Blom, Peggy I Wang, Jung Eun Shim, Edward M Marcotte

    Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

    Genome Res. 2011:21(7)

    260 Citations (from Europe PMC, 2019-01-19)

  • Prioritizing candidate disease genes by network-based boosting of genome-wide association data. [PMID: 21536720]
    Insuk Lee, U Martin Blom, Peggy I Wang, Jung Eun Shim, Edward M Marcotte

    Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

    Genome Res. 2011:21(7)

    260 Citations (from Europe PMC, 2019-01-19)

Word cloud

Prioritizing candidate disease genes network-based boosting genome-wide association data Network "guilt association" GBA proven approach identifying novel disease genes based observation similar mutational phenotypes arise functionally related genes In principle approach could account even nonadditive genetic interactions which underlie synergistic combinations mutations often linked complex diseases Here analyze large-scale human gene functional interaction network dubbed HumanNet We show candidate disease genes can be effectively identified GBA cross-validated tests using label propagation algorithms related Google's PageRank However GBA has been shown work poorly genome-wide association studies GWAS where many genes somewhat implicated but few known very high certainty Here resolve explicitly modeling uncertainty associations incorporating uncertainty seed set into GBA framework We observe significant boost power detect validated candidate genes Crohn's disease type 2 diabetes comparing our predictions results follow-up meta-analyses incorporation network serving highlight JAK-STAT pathway associated adaptors GRB2/SHC1 Crohn's disease BACH2 type 2 diabetes Consideration network during GWAS thus conveys some benefits enrolling more participants GWAS study More generally demonstrate functional network human genes provides valuable statistical framework prioritizing candidate disease genes both candidate gene-based GWAS-based studies
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  • Created on: 2019-01-04
    • ***d@***c.cn [2019-01-11]
    • ***d@***c.cn [2019-01-11]
    • ***d@***c.cn [2019-01-04]

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