{"id":"W2016928406","doi":"10.1093/bioinformatics/bth451","title":"Discovering patterns to extract protein–protein interactions from full texts","year":2004,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":238,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Recall rate; Scientific literature; Matching (statistics); Information retrieval; Natural language processing; Artificial intelligence; Data mining; Biology","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":[0.001607278,0.001359032,0.0009564547,0.008935859,0.0007376109,0.00165141,0.001368704,0.001199044,0.003306407],"category_scores_gemma":[0.008883455,0.0005236529,0.001060543,0.005856973,0.0006311505,0.003637616,0.001411308,0.0009292141,0.003513576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004465186,"about_ca_system_score_gemma":0.00108881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009805065,"about_ca_topic_score_gemma":0.001591971,"domain_scores_codex":[0.9983241,0.0002974941,0.00032064,0.0005624424,0.0004134979,0.00008186045],"domain_scores_gemma":[0.9932368,0.004227547,0.0008915794,0.0005086994,0.0009319963,0.0002033694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006136655,0.0006755263,0.02397599,0.002244829,0.000289402,0.001478384,0.0008317591,0.005176969,0.06321051,0.00621155,0.01548447,0.879807],"study_design_scores_gemma":[0.0004205076,0.0008642552,0.05173383,0.0006936747,0.001027924,0.01038029,0.002551163,0.5369634,0.2139628,0.09066459,0.09051912,0.0002184531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1401553,0.002222407,0.8256353,0.001515233,0.00011106,0.0008665443,0.01234066,0.01188825,0.005265271],"genre_scores_gemma":[0.1415841,0.0009543115,0.8376223,0.0001871007,0.0001110673,0.0004783807,0.01663641,0.0003359506,0.002090437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008935859,"threshold_uncertainty_score":0.01106107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01549990955994195,"score_gpt":0.267165727324153,"score_spread":0.251665817764211,"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."}}