{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002913309,0.0007642996,0.00124739,0.001706021,0.0007884863,0.001469694,0.001491611,0.001395968,0.001762333],"category_scores_gemma":[0.01542285,0.0007040271,0.001014635,0.001884396,0.0009145087,0.001404644,0.00155505,0.001775848,0.0004169235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007963766,"about_ca_system_score_gemma":0.001039568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002434882,"about_ca_topic_score_gemma":0.003371203,"domain_scores_codex":[0.9984394,0.0006675389,0.000089869,0.0004835149,0.0002322982,0.00008740499],"domain_scores_gemma":[0.9914785,0.006161212,0.0009382514,0.000675465,0.0004435906,0.0003030144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000519363,0.00009550799,0.06872291,0.0003771381,0.0003245736,0.0007671125,0.0002764028,0.81392,0.02618374,0.01234199,0.002640311,0.07383101],"study_design_scores_gemma":[0.00003010065,0.00003949388,0.004173824,0.00002860148,0.00004517441,0.0003247084,0.00003391402,0.9674817,0.004653782,0.02184294,0.001327165,0.0000186986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2067012,0.0006238822,0.7879759,0.0004264394,0.00002784603,0.00008160478,0.001972797,0.0009981206,0.00119222],"genre_scores_gemma":[0.7478815,0.0002533022,0.247286,0.0001998299,0.00005211174,0.0001191726,0.003523527,0.0001980582,0.0004864473],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002913309,"threshold_uncertainty_score":0.01540726,"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."}}