{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000567347,0.0001965838,0.0004348812,0.00002654129,0.0001010536,0.000005371265,0.0002125693,0.0003337822,0.00001849887],"category_scores_gemma":[0.0002654889,0.0001932233,0.000134876,0.0001480625,0.0001397741,0.000002421855,0.0001343666,0.0001634916,0.000003862423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001043215,"about_ca_system_score_gemma":0.00008559533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001275013,"about_ca_topic_score_gemma":0.000003251312,"domain_scores_codex":[0.9980854,0.0003634581,0.0006800798,0.0003636953,0.00006875014,0.0004385998],"domain_scores_gemma":[0.9990849,0.00008024902,0.0003789006,0.0002478065,0.00006075713,0.0001473312],"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":[0.00007612655,0.00002029796,0.03369735,0.00009991461,0.00007518373,7.663225e-7,0.00006942161,0.9187535,0.02453155,0.0004981036,0.0004524069,0.02172536],"study_design_scores_gemma":[0.0006563685,0.0004230614,0.006207752,0.00001634619,0.00006397966,0.00001667243,0.00004496221,0.9774777,0.00127641,0.0008508394,0.01265745,0.0003084994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1161052,0.007679553,0.8750821,0.0002602019,0.0001481983,0.0002294389,0.000007248486,0.00001900309,0.0004690247],"genre_scores_gemma":[0.7989236,0.0004861921,0.1983363,0.001453345,0.000513425,0.000009945205,0.0002303588,0.00002804337,0.00001880116],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6828184,"threshold_uncertainty_score":0.7879417,"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."}}