{"id":"W2154264671","doi":"10.1186/1471-2164-13-s4-s10","title":"Automated extraction and semantic analysis of mutation impacts from the biomedical literature","year":2012,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ontology; Heuristics; Information retrieval; Mutation; Biomedical text mining; Information extraction; Domain (mathematical analysis); Precision and recall; Data mining; Task (project management); Stability (learning theory); Computational biology; Bioinformatics; Biology; Text mining; Genetics; Machine learning; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002754256,0.001466136,0.0009926888,0.0360548,0.001186573,0.002789922,0.001223077,0.001480732,0.003304972],"category_scores_gemma":[0.01432111,0.0004663155,0.001784877,0.01142741,0.000731874,0.003552479,0.002418251,0.0009093657,0.002343041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001438076,"about_ca_system_score_gemma":0.003548263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004067525,"about_ca_topic_score_gemma":0.005938966,"domain_scores_codex":[0.9967005,0.0003795701,0.0006660844,0.0008086117,0.001305377,0.0001398161],"domain_scores_gemma":[0.9874393,0.00603202,0.002372977,0.0009593457,0.002858949,0.0003373641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006192061,0.0005501627,0.04810779,0.01071192,0.0007202321,0.005643506,0.002249482,0.00613575,0.09606902,0.01163257,0.07494397,0.7426164],"study_design_scores_gemma":[0.0002977416,0.0005640905,0.1885439,0.00350427,0.002599344,0.01210808,0.004602794,0.1986325,0.1603872,0.0466942,0.3815884,0.000477494],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3112459,0.01820617,0.3984593,0.005396907,0.001006535,0.002325506,0.1752272,0.06700025,0.02113226],"genre_scores_gemma":[0.2989948,0.006594196,0.5399411,0.0007492832,0.0005406503,0.0005982866,0.1482761,0.001347329,0.002958215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0360548,"threshold_uncertainty_score":0.01456606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537149552956209,"score_gpt":0.2897046117357931,"score_spread":0.274333116206231,"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."}}