{"id":"W2962028921","doi":"10.1093/bioinformatics/btz355","title":"Collaborative intra-tumor heterogeneity detection","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; Simon Fraser University","funders":"","keywords":"Computer science; Inference; Phylogenetic tree; Deconvolution; Source code; Process (computing); Data mining; Computational biology; Machine learning; Artificial intelligence; Biology; Algorithm; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008401358,0.0001115192,0.0001024849,0.00003009344,0.00004650506,0.00004070793,0.0001069049,0.00007526792,0.00002128859],"category_scores_gemma":[0.00005472873,0.0001080434,0.00004972577,0.00009434408,0.00002880491,0.000006158512,0.00007503789,0.00005085994,0.0001860005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000262012,"about_ca_system_score_gemma":0.00009039281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005845326,"about_ca_topic_score_gemma":0.00005847855,"domain_scores_codex":[0.9994179,0.00001055482,0.0002014368,0.0001162392,0.00008837244,0.000165485],"domain_scores_gemma":[0.9994508,0.00001106051,0.0001001691,0.000278339,0.00009589523,0.00006371911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005636936,0.0002279318,0.01999889,0.0003758557,0.0003321774,0.000005864459,0.000930263,0.002463093,0.8994079,0.001023846,0.0151262,0.0595443],"study_design_scores_gemma":[0.000896866,0.0006332622,0.002654508,0.00001253843,0.00001873649,0.00002343466,0.0004062822,0.004377111,0.7655974,0.0000522304,0.2249684,0.0003591822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903882,0.0001855414,0.003669668,0.00002910137,0.0004951032,0.0002849461,0.00006695632,0.0000142707,0.004866235],"genre_scores_gemma":[0.9961103,0.0001325903,0.00272354,0.0006630821,0.0001456603,0.00001314493,0.00007214586,0.00001310778,0.0001264789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2098422,"threshold_uncertainty_score":0.4405884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004623206389557855,"score_gpt":0.2230465216424029,"score_spread":0.218423315252845,"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."}}