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Cited by 9Biomedical Text Mining and OntologiesBioinformatics and Genomic NetworksComputational Drug Discovery Methods

Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models

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Millikin, Robert J., Raja, Kalpana, Steill, John, Lock, Cannon, Tu, Xuancheng, Ross, Ian, Tsoi, Lam C., Kuusisto, Finn, Ni, Zijian, Livny, Miron, Bockelman, Brian, Thomson, James, Stewart, Ron·2023·BMC Bioinformatics
DOI 10.1186/s12859-023-05539-yPMC PMC10619245

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Abstract

Background: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A–B–C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results: We demonstrate SKiM’s ability to discover useful A–B–C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

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Authors
Millikin, R. J., Raja, K., Steill, J., Lock, C., Tu, X., Ross, I., Tsoi, L. C., Kuusisto, F., Ni, Z., Livny, M., Bockelman, B., Thomson, J., & Stewart, R.
Affiliations
  • Morgridge Institute for Research (US)
  • University of Wisconsin–Madison (US)
  • University of Michigan (US)
  • Amazon (United States) (US)
Corresponding author
Stewart, Ron
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2023-11-01
Volume
24
Issue
1
Pages
412-412
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-023-05539-y
PMC
PMC10619245
OpenAlex
W4388139915
Cited by
9
References
0
Citations by year
2026: 2 · 2025: 3 · 2024: 4
FWCI
0.78
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. National Library of Medicine (T15 LM007359)
  • National Science Foundation (ICER 1343760)
  • National Institutes of Health (K01AR07212, NIH GM102756)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
literature-based discovery, biomedical knowledge discovery, co-occurrence analysis, transformer models, knowledge graphs, drug repurposing, cancer association, PubMed

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