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Typically, researchers generate hypotheses by carefully analyzing available information and making logical connections, which are then tested. The accelerating growth of biomedical literature makes it increasingly difficult to keep pace with connections between biological entities emerging across biomedical research. Recently developed automated means of generating hypotheses can generate many more hypotheses than can be easily tested. One such approach involves literature\u2011based discovery (LBD) systems such as Serial KinderMiner (SKiM), which surfaces putative\n                      <jats:italic>A\u2011B\u2011C<\/jats:italic>\n                      links derived from term co\u2011occurrence. However, LBD systems leave three critical gaps: (i) they find statistical associations, not biological relationships; (ii) they can produce false\u2011positive leads; and (iii) they do not assess agreement with a hypothesis in question. As a result, LBD search results often require costly manual curation to be of practical utility to the researcher. Large language models (LLMs) have the potential to automate much of this curation step, but standalone LLMs are hampered by hallucinations, lack of transparency in information sources, and the inability to reference data not included in the training corpus.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We introduce SKiM-GPT, a retrieval-augmented generation (RAG) system that combines SKiM\u2019s co-occurrence search and retrieval with frontier LLMs to evaluate user-defined hypotheses. For every chosen\n                      <jats:italic>A<\/jats:italic>\n                      -\n                      <jats:italic>B<\/jats:italic>\n                      -\n                      <jats:italic>C<\/jats:italic>\n                      SKiM hit, SKiM-GPT retrieves appropriate PubMed abstract texts, filters out irrelevant abstracts with a fine-tuned relevance model, and prompts an LLM to evaluate the user\u2019s hypothesis, given the relevant abstracts. Importantly, the SKiM-GPT system is transparent and human-verifiable: it displays the retrieved abstracts, the hypothesis score, and a justification for the score grounded in the texts and written in natural language. On a benchmark consisting of 14 disease-gene-drug hypotheses, SKiM-GPT achieves strong ordinal agreement with four expert biologists (Cohen\u2019s \u03ba\u2009=\u20090.84), demonstrating its ability to replicate expert judgment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      SKiM-GPT is open-source (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/stewart-lab\/skimgpt\" ext-link-type=\"uri\">https:\/\/github.com\/stewart-lab\/skimgpt<\/jats:ext-link>\n                      ) and available through a web interface (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/skim.morgridge.org\" ext-link-type=\"uri\">https:\/\/skim.morgridge.org<\/jats:ext-link>\n                      ), enabling both wet-lab and computational researchers to systematically and efficiently evaluate biomedical hypotheses at scale.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06350-7","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T18:43:57Z","timestamp":1765997037000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation"],"prefix":"10.1186","volume":"27","author":[{"given":"Jack","family":"Freeman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J.","family":"Millikin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ishaan","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bethany","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cannon","family":"Lock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Shine George","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aviral","family":"Bal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chitrasen","family":"Mohanty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ron","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"6350_CR1","doi-asserted-by":"publisher","unstructured":"Abdin M, Aneja J, Awadalla H, Awadallah A, Awan AA, Bach N, et al. 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This work analyzed publicly available biomedical abstracts and did not involve human participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable. The manuscript does not contain any individual person\u2019s data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16"}}