{"id":"W2192492580","doi":"10.1007/978-3-662-44753-6_27","title":"Reconstructing Mutational History in Multiply Sampled Tumors Using Perfect Phylogeny Mixtures","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Phylogenetics; Algorithm; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003637291,0.0002907744,0.0002908584,0.0002833678,0.0000792639,0.00004554349,0.0004344998,0.0002520221,0.00002303698],"category_scores_gemma":[0.0002893085,0.0003096013,0.00009480609,0.00007000707,0.0004553917,0.000004481653,0.0002595936,0.000310612,0.000002297201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003899345,"about_ca_system_score_gemma":0.0007811328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001215809,"about_ca_topic_score_gemma":0.0003680021,"domain_scores_codex":[0.998294,0.00002492768,0.0003344238,0.0007998987,0.0002191774,0.0003275684],"domain_scores_gemma":[0.9990537,0.0001896242,0.0002021211,0.0003716923,0.0001092246,0.00007370953],"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":[0.0001150418,0.00004917117,0.007263547,0.0001436329,0.00004993382,0.000116448,0.0007887036,0.3846415,0.2579638,0.001231034,0.0001416745,0.3474955],"study_design_scores_gemma":[0.004576623,0.001252256,0.005634518,0.001894493,0.00009890337,0.001216445,0.000004402911,0.7984217,0.1064073,0.04127528,0.03378174,0.005436325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1619758,0.003086251,0.8317204,0.000044493,0.001977042,0.0003357086,0.00002860663,0.00001309116,0.000818565],"genre_scores_gemma":[0.9053701,0.00004589364,0.09226936,0.001105446,0.001046441,0.000005209407,0.00005179978,0.00004069275,0.00006510861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7433942,"threshold_uncertainty_score":0.9999356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777973983632999,"score_gpt":0.2393847600744556,"score_spread":0.2216050202381256,"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."}}