{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:55:22Z","timestamp":1780624522906,"version":"3.54.1"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health USA","doi-asserted-by":"crossref","award":["U01 AG066833"],"award-info":[{"award-number":["U01 AG066833"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health USA","doi-asserted-by":"crossref","award":["R01 LM010098"],"award-info":[{"award-number":["R01 LM010098"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Answering and solving complex problems using a large language model (LLM) given a certain domain such as biomedicine is a challenging task that requires both factual consistency and logic, and LLMs often suffer from some major limitations, such as hallucinating false or irrelevant information, or being influenced by noisy data. These issues can compromise the trustworthiness, accuracy, and compliance of LLM-generated text and insights.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Knowledge Retrieval Augmented Generation ENgine (KRAGEN) is a new tool that combines knowledge graphs, Retrieval Augmented Generation (RAG), and advanced prompting techniques to solve complex problems with natural language. KRAGEN converts knowledge graphs into a vector database and uses RAG to retrieve relevant facts from it. KRAGEN uses advanced prompting techniques: namely graph-of-thoughts (GoT), to dynamically break down a complex problem into smaller subproblems, and proceeds to solve each subproblem by using the relevant knowledge through the RAG framework, which limits the hallucinations, and finally, consolidates the subproblems and provides a solution. KRAGEN\u2019s graph visualization allows the user to interact with and evaluate the quality of the solution\u2019s GoT structure and logic.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>KRAGEN is deployed by running its custom Docker containers. KRAGEN is available as open-source from GitHub at: https:\/\/github.com\/EpistasisLab\/KRAGEN.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae353","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T20:52:45Z","timestamp":1717447965000},"source":"Crossref","is-referenced-by-count":89,"title":["KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models"],"prefix":"10.1093","volume":"40","author":[{"given":"Nicholas","family":"Matsumoto","sequence":"first","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Moran","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunjun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel E","family":"Hernandez","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mythreye","family":"Venkatesan","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069, United 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