{"id":"W4387665935","doi":"10.21203/rs.3.rs-3385041/v1","title":"Cancer driver genes and opportunities for precision oncology revealed by whole genome sequencing 10,478 cancers","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Cancer Research","funders":"Medical Research Council; National Institute for Health and Care Research; Royal Marsden NHS Foundation Trust; Department of Health and Social Care; Cancer Research UK; Wellcome Trust","keywords":"Precision oncology; Whole genome sequencing; Genome; Cancer; Gene; Precision medicine; Cancer genome sequencing; Computational biology; Oncology; Medicine; DNA sequencing; Clinical trial; Internal medicine; Bioinformatics; Biology; Genetics; Pathology","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":[0.000778518,0.0005498546,0.0006495354,0.001003151,0.0004669446,0.001642095,0.0002929055,0.0008150914,0.005099661],"category_scores_gemma":[0.001874885,0.0005400407,0.0006242652,0.001946092,0.0003766307,0.0004288682,0.0005683549,0.0008685241,0.001457121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006829915,"about_ca_system_score_gemma":0.001099077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993508,"about_ca_topic_score_gemma":0.006493583,"domain_scores_codex":[0.9993481,0.00004986307,0.00003021926,0.0002310393,0.000229755,0.0001108625],"domain_scores_gemma":[0.9994515,0.0002372781,0.00006736042,0.00009604664,0.00008422574,0.00006367541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002370581,0.0001293215,0.2309199,0.001796172,0.001493086,0.003038573,0.001082587,0.005655893,0.2900232,0.01931878,0.0718001,0.372372],"study_design_scores_gemma":[0.0002600667,0.000239231,0.5071802,0.0004113006,0.00263896,0.005209765,0.0008059475,0.00748247,0.09151447,0.02350636,0.3606224,0.0001287738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8753349,0.02876258,0.01373961,0.003286859,0.0004874816,0.00005875554,0.05341204,0.00150797,0.02340989],"genre_scores_gemma":[0.9226493,0.01205429,0.01365662,0.0009857464,0.0001850775,0.00005804619,0.0358264,0.0007869068,0.01379763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005099661,"threshold_uncertainty_score":0.01706004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1520340661992253,"score_gpt":0.4125636183102442,"score_spread":0.2605295521110189,"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."}}