Biomedical discovery through the integrative biomedical knowledge hub (iBKH).

Autor:	Su C; Department of Health Service Administration and Policy, College of Public Health, Temple University, Philadelphia, PA 19122, USA., Hou Y; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA.; Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA., Zhou M; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA., Rajendran S; Tri-Institutional Computational Biology & Medicine Program, Cornell University, New York, NY 10065, USA., Maasch JRMA; Department of Computer Science, Cornell Tech, New York, NY 10044, USA., Abedi Z; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA., Zhang H; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA., Bai Z; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA., Cuturrufo A; Computer Science, Cornell University, Ithaca, NY 14850, USA., Guo W; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA., Chaudhry FF; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA., Ghahramani G; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA., Tang J; Mila-Quebec AI Institute and HEC Montreal, Montreal, QC H2S 3H1, Canada., Cheng F; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA., Li Y; School of Computer Science, McGill University, Montreal, QC H3A 0C6, Canada., Zhang R; Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA., DeKosky ST; Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32610, USA., Bian J; Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA., Wang F; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA.
Jazyk:	angličtina
Zdroj:	IScience [iScience] 2023 Mar 21; Vol. 26 (4), pp. 106460. Date of Electronic Publication: 2023 Mar 21 (Print Publication: 2023).
DOI:	10.1016/j.isci.2023.106460
Abstrakt:	The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available. Competing Interests: The authors declare no competing interests. (© 2023 The Authors.)
Databáze:	MEDLINE
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