{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006107984,0.001096026,0.0006764515,0.00162862,0.0002472522,0.0006217583,0.0009518489,0.0008445632,0.00148861],"category_scores_gemma":[0.001190597,0.0004457112,0.0008546578,0.0006450468,0.000213151,0.0006306944,0.0006819941,0.0008617538,0.0004676003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000806716,"about_ca_system_score_gemma":0.001156989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009860744,"about_ca_topic_score_gemma":0.01400104,"domain_scores_codex":[0.999773,0.00003842246,0.00001647513,0.00007012297,0.00005724105,0.00004485949],"domain_scores_gemma":[0.9996922,0.0001282141,0.00005761893,0.00002941991,0.00005815868,0.00003448786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006800279,0.000529715,0.06454798,0.0003733714,0.000648523,0.0009841397,0.0001040638,0.416137,0.0348429,0.004467356,0.01922967,0.4574553],"study_design_scores_gemma":[0.00002707563,0.00004372385,0.00218288,0.0000107173,0.00003195834,0.000117728,0.000008344412,0.9895681,0.004876224,0.002033496,0.0010866,0.00001309911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4081766,0.005465013,0.5576779,0.001256893,0.000190218,0.0002364727,0.00625836,0.01668855,0.004049965],"genre_scores_gemma":[0.8469597,0.001314639,0.1365843,0.0004878066,0.0000715796,0.0001496237,0.009615476,0.0002278414,0.004588991],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009860744,"threshold_uncertainty_score":0.01960671,"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."}}