{"id":"W3006330121","doi":"10.1002/gepi.22282","title":"PANDA: Prioritization of autism‐genes using network‐based deep‐learning approach","year":2020,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autism; Computer science; Computational biology; Gene; Artificial intelligence; Gene regulatory network; Classifier (UML); Prioritization; Exome sequencing; Machine learning; Human genome; Deep learning; Ranking (information retrieval); Biology; Genetics; Genome; Mutation; Medicine","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":[0.0005919156,0.001039598,0.0007213985,0.00198965,0.0005017242,0.0006413626,0.0008699301,0.0007758201,0.00338739],"category_scores_gemma":[0.00157542,0.0003186681,0.0007513969,0.0007173855,0.0002270836,0.0005382978,0.0008918548,0.0008363038,0.000673635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007659183,"about_ca_system_score_gemma":0.001432248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00838747,"about_ca_topic_score_gemma":0.01335303,"domain_scores_codex":[0.9997452,0.00006361205,0.00001645506,0.00007369773,0.00005539766,0.00004561735],"domain_scores_gemma":[0.9995147,0.0002316232,0.00005732721,0.00003535463,0.000118374,0.00004274685],"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":[0.001131702,0.0007993751,0.03893024,0.0005534776,0.0006703993,0.0006449111,0.0001624514,0.3333302,0.02775637,0.007618853,0.02838056,0.5600214],"study_design_scores_gemma":[0.00004889569,0.0001037729,0.002850122,0.00001720571,0.00006718312,0.0001395029,0.00002959156,0.9839559,0.003911831,0.006536135,0.002323613,0.00001622147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.236524,0.002126327,0.7315328,0.001692323,0.0002095394,0.0004342837,0.006623779,0.01518275,0.005674146],"genre_scores_gemma":[0.7006514,0.0005918862,0.2833918,0.0006820538,0.00008466125,0.0004075294,0.008344525,0.0002694735,0.005576535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00838747,"threshold_uncertainty_score":0.01667732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03747717373290918,"score_gpt":0.2719373131388289,"score_spread":0.2344601394059197,"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."}}