{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/moliere-automatic-biomedical-hypothesis","title":"MOLIERE: Automatic Biomedical Hypothesis Generation System","arxiv_id":"1702.06176","date":"2017-05-31","proceeding":null,"authors":["Sybrandt Justin","Shtutman Michael","Safro Ilya"],"abstract":"Hypothesis generation is becoming a crucial time-saving technique which\nallows biomedical researchers to quickly discover implicit connections between\nimportant concepts. Typically, these systems operate on domain-specific\nfractions of public medical data. MOLIERE, in contrast, utilizes information\nfrom over 24.5 million documents. At the heart of our approach lies a\nmulti-modal and multi-relational network of biomedical objects extracted from\nseveral heterogeneous datasets from the National Center for Biotechnology\nInformation (NCBI). These objects include but are not limited to scientific\npapers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses\nusing Latent Dirichlet Allocation applied on abstracts found near shortest\npaths discovered within this network, and demonstrate the effectiveness of\nMOLIERE by performing hypothesis generation on historical data. Our network,\nimplementation, and resulting data are all publicly available for the broad\nscientific community.","url_abs":"http://arxiv.org/abs/1702.06176v3","url_pdf":"http://arxiv.org/pdf/1702.06176v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"moliere-automatic-biomedical-hypothesis","repo_url":"https://github.com/JSybrandt/MOLIERE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.06176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.06176"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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