{"id":"W3217102710","doi":"10.1101/2021.11.22.469566","title":"Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Leverage (statistics); Inference; Computer science; Artificial intelligence; Machine learning; Selection (genetic algorithm); Transfer of learning; Bayesian inference; Deep learning; Cancer; Genomics; ENCODE; Computational biology; Bayesian probability; Biology; Genome; Gene; Genetics","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":[0.001568,0.0003665476,0.0003110355,0.0003890885,0.0002266551,0.0005651467,0.0009164067,0.000909682,0.0009932863],"category_scores_gemma":[0.006246301,0.00033529,0.0005832284,0.0002611121,0.0006812974,0.000500703,0.0005381753,0.0008409919,0.0002423948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006808083,"about_ca_system_score_gemma":0.0005577134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00278259,"about_ca_topic_score_gemma":0.005212463,"domain_scores_codex":[0.9996378,0.0001423923,0.00001347952,0.0001101657,0.00007133236,0.00002479644],"domain_scores_gemma":[0.9968885,0.002194501,0.0002458029,0.0003647711,0.0002094073,0.00009714857],"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":[0.0001971892,0.0000609563,0.01538257,0.00009230022,0.00009255302,0.0001746728,0.0000866796,0.9393759,0.01028757,0.007577382,0.00180536,0.02486685],"study_design_scores_gemma":[0.000009240724,0.00001238553,0.0008098832,0.000005103499,0.000005909738,0.00004324781,0.000008917986,0.9897749,0.00367524,0.005180475,0.0004687772,0.000005874182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4431639,0.0003978156,0.5502911,0.0006403196,0.00007328398,0.00006014405,0.001717566,0.001793712,0.00186227],"genre_scores_gemma":[0.8809054,0.0001178004,0.1147164,0.0002348716,0.00003187787,0.00007620154,0.002332609,0.0001755795,0.001409125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00278259,"threshold_uncertainty_score":0.008292496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551704794811444,"score_gpt":0.2232279800280368,"score_spread":0.2077109320799224,"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."}}