{"id":"W2977291034","doi":"","title":"Subpoplar: reconstructing cancer phylogenies by ordering mutation pairs","year":2018,"lang":"en","type":"article","venue":"Research in Computational Molecular Biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mutation; Computational biology; Evolutionary biology; Computer science; Genetics; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00159826,0.00118248,0.001284645,0.003868472,0.00105298,0.001534235,0.001968715,0.001687196,0.01101328],"category_scores_gemma":[0.007580921,0.0009847836,0.002163008,0.002346317,0.0007966627,0.001131262,0.002002913,0.001724467,0.002545667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005894956,"about_ca_system_score_gemma":0.001270497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005334967,"about_ca_topic_score_gemma":0.01052271,"domain_scores_codex":[0.9994117,0.0002135519,0.00002709048,0.0001940448,0.0001014299,0.00005232306],"domain_scores_gemma":[0.9971702,0.001953153,0.0001365311,0.0004776512,0.0001316937,0.0001307583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004057884,0.0005479157,0.05901608,0.001787006,0.002739388,0.002074386,0.0009802858,0.288048,0.02374468,0.03526817,0.09742752,0.4843086],"study_design_scores_gemma":[0.0005678968,0.000193795,0.003718009,0.0000927124,0.0004070124,0.0008680898,0.0001794873,0.9272921,0.004576109,0.04663868,0.01540724,0.00005883477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1953167,0.002660496,0.7032846,0.001581546,0.0004323986,0.0003715956,0.02999827,0.06096313,0.005391312],"genre_scores_gemma":[0.5182247,0.0009297554,0.4336964,0.0007218668,0.0002402281,0.0004277564,0.03733671,0.004693258,0.003729287],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01101328,"threshold_uncertainty_score":0.03684306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600871711862112,"score_gpt":0.3880055430303447,"score_spread":0.3519968259117236,"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."}}