{"id":"W4403012771","doi":"10.1186/s13326-024-00319-w","title":"MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed","year":2024,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Craig Newmark Philanthropies; Université de Sousse; Wikimedia Foundation; University of Dayton","keywords":"Computer science; Relation (database); Machine learning; Artificial intelligence; Data science; Information retrieval; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002499224,0.001991443,0.001199198,0.02003872,0.001178266,0.002175799,0.00186699,0.001499867,0.00554274],"category_scores_gemma":[0.01418642,0.0004548721,0.001658195,0.01147966,0.0004461239,0.004579489,0.003345016,0.001130901,0.004404401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001567153,"about_ca_system_score_gemma":0.003458215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009602194,"about_ca_topic_score_gemma":0.0240587,"domain_scores_codex":[0.9969119,0.0005795168,0.0005520578,0.0008554821,0.0009447977,0.0001562815],"domain_scores_gemma":[0.9941548,0.003115826,0.0008222255,0.0008611423,0.0007761258,0.0002699765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000859355,0.0005808044,0.0498241,0.005937808,0.0008647975,0.0009343551,0.0006498923,0.00806078,0.01526009,0.007400191,0.1803575,0.7292703],"study_design_scores_gemma":[0.0005015028,0.001392689,0.06432586,0.001611934,0.0009766937,0.002627739,0.001589681,0.3605979,0.0280351,0.04556996,0.4923719,0.0003989336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1644346,0.0316768,0.2846745,0.008058958,0.002573328,0.003346525,0.363041,0.1151958,0.02699849],"genre_scores_gemma":[0.1820479,0.005959992,0.5128334,0.001390423,0.0007085139,0.00206936,0.2870725,0.001232669,0.006685287],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02003872,"threshold_uncertainty_score":0.01909262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942215229220309,"score_gpt":0.2881921641784086,"score_spread":0.2587700118862056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}