{"id":"W3045868447","doi":"10.1002/widm.1383","title":"Predicting disease‐associated genes: Computational methods, databases, and evaluations","year":2020,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Princess Margaret Cancer Centre; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Identification (biology); Computer science; Computational model; Focus (optics); Machine learning; Data science; Set (abstract data type); Point (geometry); Biological data; Modelling biological systems; Artificial intelligence; Bioinformatics; Systems biology; 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":[0.0009084769,0.0002232381,0.000310824,0.00002883154,0.0002747979,0.0001151826,0.0003093145,0.00005998315,0.000009395798],"category_scores_gemma":[0.0003692217,0.0001945609,0.00006112673,0.0001073152,0.0001282622,0.00007285317,0.002957351,0.0001076463,0.000006460822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000966367,"about_ca_system_score_gemma":0.000117916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000139249,"about_ca_topic_score_gemma":0.00002639945,"domain_scores_codex":[0.9984,0.0002498546,0.0004997371,0.0005662561,0.00008009356,0.0002041132],"domain_scores_gemma":[0.9989637,0.00009156408,0.000200318,0.0004758817,0.00004665377,0.0002218952],"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":[0.0002822414,0.0002767277,0.03008259,0.001238551,0.0005084592,0.000007609388,0.004112021,0.000255368,0.002007113,0.000208469,0.1561136,0.8049073],"study_design_scores_gemma":[0.001853717,0.0005653025,0.008631499,0.002546305,0.0008315134,0.00005072572,0.0029439,0.7304514,0.00006195552,0.0004261318,0.2503223,0.001315274],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2096651,0.5050849,0.2738103,0.001579983,0.0006673261,0.001375693,0.006226441,0.00006652995,0.001523728],"genre_scores_gemma":[0.7148184,0.06330925,0.1282565,0.002200917,0.002666727,0.0002045659,0.08741625,0.0001518986,0.0009754847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.803592,"threshold_uncertainty_score":0.7933961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09762632397675337,"score_gpt":0.4011090349923812,"score_spread":0.3034827110156278,"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."}}