A method to explore the connectivity patterns of proteins and drugs for identifying disease communities
- Submitting institution
-
University of Sunderland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1186
- Type
- D - Journal article
- DOI
-
10.1007/s42979-020-00151-w
- Title of journal
- SN Computer Science
- Article number
- 137 (2020)
- First page
- -
- Volume
- 137
- Issue
- -
- ISSN
- 2661-8907
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- URL
-
http://sure.sunderland.ac.uk/id/eprint/11905/
- Supplementary information
-
-
- Request cross-referral to
- 3 - Allied Health Professions, Dentistry, Nursing and Pharmacy
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The reason for common drug side-effects are likely to be shared pathways and proteins which in turn leads to a network of diseases. Using validated and verified protein to disease associations we constructed a complex web of interacting diseases that may benefit from repurposed drugs. We have been able to identify various modules that make sense from biological and medical perspectives. The research has created opportunities to extend the use of AI into the virtual screening aspect of the drug discovery process, reducing the computational burden. This is being explored by pharmaceutical manufacturer Chengdu Yinto Tech Co. Ltd (yugong@yontinotech.com).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -