{"id":"W4291291776","doi":"10.1186/s13578-022-00871-x","title":"OpenVar: functional annotation of variants in non-canonical open reading frames","year":2022,"lang":"en","type":"article","venue":"Cell & Bioscience","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"PROTEO; Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Canada Research Chairs; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Compute Canada; Université de Sherbrooke","keywords":"Annotation; Open reading frame; Reading (process); Computer science; Biology; Computational biology; Information retrieval; Artificial intelligence; Genetics; Linguistics","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":[0.0002406765,0.00005634028,0.00007268041,0.00003993732,0.0001196399,0.00002950106,0.0004148534,0.00002682068,0.000100245],"category_scores_gemma":[0.00003208834,0.0000566487,0.00002801161,0.0001788364,0.000064961,0.000007382477,0.0005530083,0.0000513519,0.000002824501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001717444,"about_ca_system_score_gemma":0.0002233327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008639517,"about_ca_topic_score_gemma":0.00001037958,"domain_scores_codex":[0.999315,0.00003383862,0.0001341548,0.0002730431,0.0001154654,0.000128484],"domain_scores_gemma":[0.9996875,0.000008715029,0.00006496433,0.0001693426,0.00002719114,0.00004232099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005083988,0.0001397594,0.005669817,0.000005467917,0.000002006548,0.000004962134,0.00005036939,0.0009459472,0.9917231,0.0001487726,0.0008718499,0.0003871624],"study_design_scores_gemma":[0.00195242,0.001021307,0.634914,0.00002219253,0.00001848075,0.00003658344,0.00131792,0.003137307,0.3272914,0.0006089385,0.02911274,0.000566719],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955705,0.0001081319,0.0004463285,0.00007663122,0.0001896217,0.0001515068,0.00004155944,0.000001315916,0.003414383],"genre_scores_gemma":[0.9985201,0.00002341558,0.0005438321,0.0002364554,0.0000202628,0.00002307577,0.00005924913,0.000004761579,0.0005688217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6644316,"threshold_uncertainty_score":0.2310067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301291806527359,"score_gpt":0.2537648422398657,"score_spread":0.2407519241745921,"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."}}