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

doi:10.1016/j.isci.2023.106460

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

Surgery

Research output: Contribution to journal › Article › peer-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 language	English (US)
Article number	106460
Journal	iScience
Volume	26
Issue number	4
DOIs	https://doi.org/10.1016/j.isci.2023.106460
State	Published - Apr 21 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

Bioinformatics
Drugs
Systems biology

Access

10.1016/j.isci.2023.106460

OpenUrl availability

Full text

Cite this

Su, C., Hou, Y., Zhou, M., Rajendran, S., Maasch, J. R. M. A., Abedi, Z., Zhang, H., Bai, Z., Cuturrufo, A., Guo, W., Chaudhry, F. F., Ghahramani, G., Tang, J., Cheng, F., Li, Y., Zhang, R., DeKosky, S. T., Bian, J., & Wang, F. (2023). Biomedical discovery through the integrative biomedical knowledge hub (iBKH). iScience, 26(4), Article 106460. https://doi.org/10.1016/j.isci.2023.106460

@article{4f95cd5ef93c4392bc404cda18e72621,

title = "Biomedical discovery through the integrative biomedical knowledge hub (iBKH)",

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.",

keywords = "Bioinformatics, Drugs, Systems biology",

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

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = apr,

day = "21",

doi = "10.1016/j.isci.2023.106460",

language = "English (US)",

volume = "26",

journal = "iScience",

issn = "2589-0042",

publisher = "Elsevier Inc.",

number = "4",

}

TY - JOUR

T1 - Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

AU - Su, Chang

AU - Hou, Yu

AU - Zhou, Manqi

AU - Rajendran, Suraj

AU - Maasch, Jacqueline R.M.A.

AU - Abedi, Zehra

AU - Zhang, Haotan

AU - Bai, Zilong

AU - Cuturrufo, Anthony

AU - Guo, Winston

AU - Chaudhry, Fayzan F.

AU - Ghahramani, Gregory

AU - Tang, Jian

AU - Cheng, Feixiong

AU - Li, Yue

AU - Zhang, Rui

AU - DeKosky, Steven T.

AU - Bian, Jiang

AU - Wang, Fei

PY - 2023/4/21

Y1 - 2023/4/21

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Drugs

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=85151290517&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85151290517&partnerID=8YFLogxK

U2 - 10.1016/j.isci.2023.106460

DO - 10.1016/j.isci.2023.106460

M3 - Article

C2 - 37020958

AN - SCOPUS:85151290517

SN - 2589-0042

VL - 26

JO - iScience

JF - iScience

IS - 4

M1 - 106460

ER -

Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

Abstract

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this