{"id":"W2908509829","doi":"","title":"Reconstructing evolutionary trajectories of mutations in cancer","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mutation; Genetics; Computational biology; Evolutionary biology; 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":[0.001245046,0.0004497843,0.0005415267,0.002204257,0.0005428678,0.001330767,0.00117002,0.001392208,0.003342043],"category_scores_gemma":[0.008757218,0.0005298202,0.0009743641,0.001572049,0.0006850055,0.001605761,0.0009673642,0.001630088,0.0005032691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206769,"about_ca_system_score_gemma":0.0007416748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01070829,"about_ca_topic_score_gemma":0.0120236,"domain_scores_codex":[0.9996548,0.0001215094,0.00002014283,0.0001036762,0.00003990254,0.0000599303],"domain_scores_gemma":[0.9960275,0.003060404,0.0002238562,0.0002912744,0.0002371577,0.0001599892],"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":[0.0003026283,0.0001394141,0.04198282,0.0001254264,0.0001887228,0.0003513283,0.0003975998,0.8421104,0.002052754,0.02381365,0.004704903,0.0838303],"study_design_scores_gemma":[0.00001642736,0.00002370885,0.001950232,0.0000181775,0.0000229098,0.00005744991,0.00008174916,0.9763376,0.0003132197,0.02048917,0.0006810625,0.000008366788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8638054,0.001074409,0.1272619,0.00219198,0.000113604,0.00004152733,0.002170329,0.000883205,0.002457646],"genre_scores_gemma":[0.9775566,0.0002795171,0.01837107,0.0001204292,0.00003171476,0.00002801785,0.001838394,0.00009087729,0.001683506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01070829,"threshold_uncertainty_score":0.02129191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03641358159742272,"score_gpt":0.3518077609820487,"score_spread":0.315394179384626,"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."}}