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A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Bonner, Stephen (författare)
AstraZeneca AB
Barrett, Ian P. (författare)
AstraZeneca AB
Ye, Cheng (författare)
AstraZeneca R&D Mölndal
visa fler...
Swiers, Rowan (författare)
AstraZeneca AB
Engkvist, Ola, 1967 (författare)
AstraZeneca AB
Bender, Andreas (författare)
University Of Cambridge
Hoyt, Charles Tapley (författare)
Harvard Medical School
Hamilton, William L. (författare)
McGill University
visa färre...
 (creator_code:org_t)
2022-09-23
2022
Engelska.
Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; In Press
  • Forskningsöversikt (refereegranskat)
Abstract Ämnesord
Stäng  
  • Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Farmaceutiska vetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Pharmaceutical Sciences (hsv//eng)
NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Samhällsfarmaci och klinisk farmaci (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Social and Clinical Pharmacy (hsv//eng)

Nyckelord

disease-gene prediction
drug-target discovery
knowledge graph embeddings

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