{"id":"W3022126255","doi":"10.1101/2020.05.06.058180","title":"Cancer phylogenetic tree inference at scale from 1000s of single cell genomes","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; University of British Columbia","funders":"","keywords":"Phylogenetic tree; Inference; Scalability; Phylogenetic network; Markov chain Monte Carlo; Tree (set theory); Computational biology; Computer science; Bayesian inference; Genome; Bayesian probability; Markov chain; Biology; Evolutionary biology; Theoretical computer science; Artificial intelligence; Machine learning; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002795819,0.0005358116,0.0008859594,0.002651271,0.0008807536,0.001268298,0.001015872,0.001029883,0.002342508],"category_scores_gemma":[0.01326054,0.0006102316,0.001409429,0.002920113,0.0004837893,0.001263473,0.001143736,0.00145485,0.0009430985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059291,"about_ca_system_score_gemma":0.00100742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006453468,"about_ca_topic_score_gemma":0.008297524,"domain_scores_codex":[0.9989169,0.0003601078,0.00006484287,0.0003783555,0.0002139147,0.00006586544],"domain_scores_gemma":[0.9956801,0.002639117,0.000272683,0.0008087764,0.0004638065,0.0001354575],"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":[0.001163363,0.0002088442,0.0692829,0.001601599,0.001675635,0.0007605577,0.000754157,0.5639443,0.06644388,0.0401687,0.04456065,0.2094353],"study_design_scores_gemma":[0.00009110493,0.00006509088,0.0193865,0.00007996456,0.0001142384,0.0002779592,0.0001772093,0.9037734,0.009161882,0.04704818,0.01977546,0.00004895567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3970865,0.002202278,0.5143131,0.001044485,0.0001641038,0.00017479,0.06754488,0.01399333,0.00347651],"genre_scores_gemma":[0.4911225,0.0007925158,0.3705958,0.0002999174,0.0000785576,0.0002612637,0.1344965,0.001173517,0.0011794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006453468,"threshold_uncertainty_score":0.01478589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643681847927837,"score_gpt":0.2171507836919584,"score_spread":0.20071396521268,"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."}}