{"id":"W2913030342","doi":"10.3389/fgene.2019.00013","title":"deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Classifier (UML); Artificial neural network; Mutation; Machine learning; Gene; Computational biology; Genetics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008312881,0.0002005614,0.0001962711,0.00008535347,0.000111372,0.00002085617,0.0001805159,0.0001446082,0.00002001144],"category_scores_gemma":[0.00001317475,0.0002147569,0.00008645315,0.0001056248,0.00009546267,0.000001310018,0.00008326863,0.0001052005,0.000001977506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006232609,"about_ca_system_score_gemma":0.00008094557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002047594,"about_ca_topic_score_gemma":0.00004926835,"domain_scores_codex":[0.9988198,0.00006187602,0.0002534666,0.0003745539,0.0001509796,0.0003393536],"domain_scores_gemma":[0.9994715,0.0000191453,0.0001044515,0.0002715089,0.00006809842,0.00006531586],"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":[0.00001964888,0.00002076815,0.4649093,0.000008894727,0.00003746515,8.415892e-7,0.00003448831,0.5244491,0.008983794,0.00000256009,0.00007443075,0.001458841],"study_design_scores_gemma":[0.0006973481,0.0001163492,0.1053371,0.00001729118,0.00003952994,0.000001906302,0.0001361043,0.8906971,0.001991465,0.00005234151,0.0006840738,0.0002294798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9519671,0.006621327,0.03941115,0.00003660645,0.001471486,0.0003291055,0.00003083842,0.000005148368,0.0001272736],"genre_scores_gemma":[0.9797812,0.0004415977,0.01909019,0.0002496154,0.0002627885,0.00002936734,0.000054319,0.00003299378,0.00005795278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.366248,"threshold_uncertainty_score":0.8757533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008630931940213957,"score_gpt":0.2291721307479415,"score_spread":0.2205411988077275,"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."}}