Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

Millikin, R.; 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.

doi:10.1101/2023.05.30.542911

bioRxiv · 10.1101/2023.05.30.542911

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

Millikin, R.·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.

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Abstract

BackgroundThe PubMed database 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 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. ResultsWe demonstrate SKiMs 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. ConclusionsSKiM 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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Millikin, R., 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.. 2023-06-01. Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models. https://doi.org/10.1101/2023.05.30.542911

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