{"doi":"10.1101/2023.05.30.542911","title":"Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models","abstract":"Background: The 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. 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":399561,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9424,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":425611,"name":"Kalpana Raja","orcid":"0000-0002-3156-4197","position":1,"is_corresponding":false},{"id":513710,"name":"John Steill","orcid":"0000-0003-2994-3400","position":2,"is_corresponding":false},{"id":1099956,"name":"Cannon Lock","orcid":null,"position":3,"is_corresponding":false},{"id":1099957,"name":"Xuancheng Tu","orcid":null,"position":4,"is_corresponding":false},{"id":47946,"name":"I. Ross","orcid":"0000-0003-1888-1689","position":5,"is_corresponding":false},{"id":12819,"name":"Lam C. Tsoi","orcid":"0000-0003-1627-5722","position":6,"is_corresponding":false},{"id":513708,"name":"Finn Kuusisto","orcid":"0000-0001-6487-4397","position":7,"is_corresponding":false},{"id":366438,"name":"Zijian Ni","orcid":"0000-0003-1181-8337","position":8,"is_corresponding":false},{"id":492054,"name":"Miron Livny","orcid":"0000-0001-5444-7439","position":9,"is_corresponding":false},{"id":77320,"name":"Brian Bockelman","orcid":"0000-0003-2981-3809","position":10,"is_corresponding":false},{"id":514325,"name":"James Thomson","orcid":null,"position":11,"is_corresponding":false},{"id":3794,"name":"Ron Stewart","orcid":"0000-0002-9041-1828","position":12,"is_corresponding":false},{"id":267547,"name":"Robert J. Millikin","orcid":"0000-0001-7440-3695","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-19T01:19:56.084411Z","pmid":"37397987","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}