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

• Published in: BMC bioinformatics Vol. 24; no. 1; p. 412
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01-11-2023; BioMed Central; BMC
• Subjects: Algorithms; Application programming interface; Biomedical text mining; Cancer; Computational linguistics; Data mining; Drugs; Employment; Humans; Knowledge; Knowledge Discovery; Knowledge graph; Knowledge management; Knowledge representation; Language; Language processing; Literature-based discovery; Machine learning; Medical research; Medicine, Experimental; Methods; Natural language; Natural language interfaces; Neoplasms; Open source software; Phenotypes; PubMed; Queries; Relation extraction; Searching; Signs and symptoms; Software; Statistical analysis; Technology application; Transformers; United States; Relation extraction; Biomedical text mining; Literature-based discovery; Knowledge graph
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05539-y

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.
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.
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.