Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

Chang Su, Yu Hou, Manqi Zhou, Suraj Rajendran, Jacqueline R.M.A. Maasch, Zehra Abedi, Haotan Zhang, Zilong Bai, Anthony Cuturrufo, Winston Guo, Fayzan F. Chaudhry, Gregory Ghahramani, Jian Tang, Feixiong Cheng, Yue Li, Rui Zhang, Steven T. DeKosky, Jiang Bian, Fei Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number106460
JournaliScience
Volume26
Issue number4
DOIs
StatePublished - Apr 21 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • Bioinformatics
  • Drugs
  • Systems biology

PubMed: MeSH publication types

  • Journal Article

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