{"id":"W2993172114","doi":"10.1186/s13073-019-0686-y","title":"Text-mining clinically relevant cancer biomarkers for curation into the CIViC database","year":2019,"lang":"en","type":"review","venue":"Genome Medicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Human Genome Research Institute; National Institutes of Health; National Cancer Institute; Compute Canada","keywords":"Data curation; Medicine; Computational biology; Data science; Bioinformatics; Computer science; Biology","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":[0.008839363,0.002071731,0.002233148,0.03034797,0.001651116,0.003903195,0.003082949,0.002870453,0.01113264],"category_scores_gemma":[0.04410602,0.0009383846,0.002013108,0.01684724,0.0007335603,0.003759719,0.00367544,0.002085685,0.006380804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00257713,"about_ca_system_score_gemma":0.00862782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005463955,"about_ca_topic_score_gemma":0.01091381,"domain_scores_codex":[0.9946169,0.001014329,0.001844904,0.001260599,0.001090465,0.0001727483],"domain_scores_gemma":[0.9618858,0.0217038,0.005629775,0.003312761,0.006384221,0.001083639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001368763,0.0006730762,0.0259172,0.05923177,0.001354217,0.006230119,0.003287748,0.007275313,0.03496258,0.01386507,0.5374271,0.3084071],"study_design_scores_gemma":[0.0005954444,0.0004604811,0.03412924,0.01106334,0.002204722,0.002907065,0.002252593,0.03002528,0.02987208,0.01597512,0.8701529,0.0003617123],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"review","genre_scores_codex":[0.0219664,0.009718816,0.05372967,0.004067258,0.0005062264,0.003125278,0.8778704,0.01858162,0.01043437],"genre_scores_gemma":[0.02877591,0.003440845,0.1967644,0.001355286,0.0002081365,0.002438247,0.7648465,0.0007523752,0.001418285],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.03034797,"threshold_uncertainty_score":0.04674762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005962690517609,"score_gpt":0.4202753033606965,"score_spread":0.3196790343089356,"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."}}