{"id":"W2002295829","doi":"10.1093/bioinformatics/btv003","title":"Clonality inference in multiple tumor samples using phylogeny","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":284,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; BC Cancer Agency","funders":"BC Cancer Agency; Natural Sciences and Engineering Research Council of Canada; Genome Canada","keywords":"Phylogenetics; Biology; Phylogenetic tree; Inference; In silico; Computational biology; Evolutionary biology; Computer science; Genetics; Gene; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002067124,0.000116864,0.0001261217,0.00004154367,0.00003223499,0.00003355927,0.0001540771,0.00007173029,0.00000377754],"category_scores_gemma":[0.0008424057,0.0001160694,0.00004032022,0.00008724468,0.00005741842,0.000006628168,0.0001499487,0.00005928304,0.00001345076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004955589,"about_ca_system_score_gemma":0.0002874618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002325968,"about_ca_topic_score_gemma":0.0003502466,"domain_scores_codex":[0.9992504,0.00001527386,0.0002968411,0.0001129213,0.0001115766,0.0002129803],"domain_scores_gemma":[0.9993956,0.00004233321,0.00009808326,0.0002629129,0.00008718728,0.0001138634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004770631,0.0004908425,0.8190572,0.0003091586,0.0001087842,0.00002446019,0.00248884,0.03898191,0.1154825,0.002023485,0.006789144,0.01376665],"study_design_scores_gemma":[0.01087737,0.001291736,0.112273,0.0002036399,0.00009388896,0.0001125056,0.004730361,0.4072638,0.1809683,0.003469208,0.2758317,0.002884587],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892208,0.0002627792,0.009418479,0.0000184533,0.0001813879,0.0001418245,0.00009682961,0.0000076212,0.0006518382],"genre_scores_gemma":[0.9755713,0.00005054113,0.02373355,0.0003932616,0.0001211198,0.00000623668,0.0001008797,0.000009833017,0.00001323439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7067842,"threshold_uncertainty_score":0.4733175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06875968615801234,"score_gpt":0.2941810204304562,"score_spread":0.2254213342724438,"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."}}