{"id":"W1835942292","doi":"10.1186/s13073-015-0188-5","title":"Explaining, not just predicting, drives interest in personal genomics","year":2015,"lang":"en","type":"article","venue":"Genome Medicine","topic":"BRCA gene mutations in cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Human Genome Research Institute; Canadian Institutes of Health Research; Cancer Research UK; National Institutes of Health; National Center for Research Resources","keywords":"Human genetics; Genomics; Personal genomics; Genome Biology; Computational biology; Computational genomics; Bioinformatics; Biology; Computer science; Data science; Medicine; Genetics; Genome; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002310858,0.0001590926,0.0002003704,0.0003842626,0.0003054182,0.001387987,0.000316752,0.000862803,0.008417919],"category_scores_gemma":[0.01368532,0.0001610019,0.0002723711,0.0004086214,0.0005359784,0.0007179863,0.0005391902,0.001088751,0.000641783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003612528,"about_ca_system_score_gemma":0.0003132316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306516,"about_ca_topic_score_gemma":0.001218059,"domain_scores_codex":[0.9986113,0.000726281,0.00007092817,0.0001297916,0.0002769601,0.0001845573],"domain_scores_gemma":[0.9721637,0.01940259,0.005375421,0.0006870303,0.0009446397,0.001426663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001085931,0.000170633,0.9823501,0.00004694633,0.0000550288,0.0001801931,0.0007639325,0.0001970476,0.000337588,0.0003489628,0.0006730288,0.01476801],"study_design_scores_gemma":[0.00001339591,0.0002118704,0.9901895,0.00005032374,0.000069596,0.00084654,0.002331394,0.002475849,0.0004077158,0.001459273,0.00192479,0.0000198545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934596,0.0002822768,0.0007234684,0.001567873,0.00001284528,0.00001549735,0.0001333681,0.00001229692,0.003792839],"genre_scores_gemma":[0.999096,0.00009307838,0.0002656059,0.0002146653,0.00002109203,0.000004499639,0.00005353857,0.000003330345,0.0002481411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008417919,"threshold_uncertainty_score":0.02816081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06731854416570486,"score_gpt":0.3084175467431972,"score_spread":0.2410990025774923,"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."}}