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HumanNet v3:用于疾病研究的人类基因网络改进数据库。

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

机构信息

Department of Biotechnology, College of Life Sciences and Biotechnology, Yonsei University, Seoul 03722, Korea.

Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

出版信息

Nucleic Acids Res. 2022 Jan 7;50(D1):D632-D639. doi: 10.1093/nar/gkab1048.

Abstract

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 (https://www.inetbio.org/humannet/, 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.

摘要

网络医学已被证明可用于剖析复杂人类疾病的遗传组织。我们之前发布了 HumanNet,这是一个用于疾病研究的人类基因综合网络。自上一版 HumanNet 发布以来,许多大型蛋白质 - 蛋白质相互作用数据集已在公共存储库中积累。此外,基因 - 表型关联的研究论文和功能注释数量也显著增加。因此,更新 HumanNet 是进一步改进基于网络的疾病研究的一项及时任务。在这里,我们展示了 HumanNet v3(https://www.inetbio.org/humannet/,涵盖 99.8%的人类蛋白编码基因),它是通过扩展数据和改进的网络推断算法构建的。HumanNet v3 支持三层模型:HumanNet-PI(蛋白质 - 蛋白质物理相互作用网络)、HumanNet-FN(功能基因网络)和 HumanNet-XC(通过共引扩展的功能网络)。用户可以根据自己的研究目的选择合适的 HumanNet 层。我们表明,在疾病基因预测方面,HumanNet v3 优于前一版 HumanNet 和其他综合人类基因网络。此外,我们证明了 HumanNet 为选择可能与 COVID-19 相关的宿主基因提供了一种可行的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62a8/8728227/d2de62f4f9ca/gkab1048fig1.jpg

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