{"id":"W4411279248","doi":"10.1093/bioinformatics/btaf344","title":"PhyClone: accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Natural Sciences and Engineering Research Council of Canada; BC Cancer Foundation; Michael Smith Health Research BC","keywords":"Bayesian probability; Computer science; Computational biology; DNA sequencing; Biology; Artificial intelligence; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006117995,0.001450965,0.00191319,0.00227912,0.001128269,0.002707241,0.0036486,0.003088248,0.006724362],"category_scores_gemma":[0.02233856,0.001923479,0.002631675,0.002448498,0.001530804,0.002640086,0.002532562,0.003665791,0.004386368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00126969,"about_ca_system_score_gemma":0.003015165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008980636,"about_ca_topic_score_gemma":0.01275409,"domain_scores_codex":[0.9985477,0.0005850318,0.00007166517,0.0004014688,0.000302458,0.00009160759],"domain_scores_gemma":[0.9940845,0.004036787,0.0003978746,0.0006883579,0.000550931,0.0002415838],"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":[0.001062653,0.0002035812,0.02759342,0.001121516,0.001010937,0.0006611518,0.0004332374,0.7198222,0.01317782,0.02555633,0.06179745,0.1475597],"study_design_scores_gemma":[0.0001020114,0.00004089298,0.001470463,0.0000651168,0.00007342009,0.0003096371,0.00002888885,0.9622301,0.002449152,0.02706464,0.006117648,0.0000479469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02242228,0.001024926,0.9525243,0.0007899154,0.0001009179,0.0001316542,0.008886368,0.01273223,0.00138741],"genre_scores_gemma":[0.2150253,0.001261555,0.7278222,0.001410648,0.0002932524,0.0006934136,0.04548402,0.004440842,0.003568734],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008980636,"threshold_uncertainty_score":0.03235549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009649039802931908,"score_gpt":0.252707724374754,"score_spread":0.2430586845718221,"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."}}