{"id":"W2952713926","doi":"10.1093/bioinformatics/bty244","title":"COSSMO: predicting competitive alternative splice site selection using deep learning","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; University of Toronto","funders":"","keywords":"splice; Computer science; RNA splicing; Sequence (biology); Alternative splicing; Computational biology; Selection (genetic algorithm); Artificial intelligence; Data mining; Gene; Biology; Genetics; RNA","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002489646,0.0001259334,0.0001061637,0.00006547443,0.0003225017,0.00004932939,0.000119603,0.0000910929,0.00002248784],"category_scores_gemma":[0.0001911364,0.0001213861,0.00004809906,0.0001459756,0.0001074668,0.00002054569,0.0001291782,0.0001602033,0.00005603106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004475089,"about_ca_system_score_gemma":0.00005467964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009632026,"about_ca_topic_score_gemma":0.0001099062,"domain_scores_codex":[0.9990641,0.00004543796,0.0002291193,0.0001429028,0.0001938197,0.0003246006],"domain_scores_gemma":[0.999355,0.00001911917,0.000141094,0.0001214673,0.0002664324,0.00009690793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001927162,0.00004743433,0.04937114,0.0000916205,0.0001693738,0.000002968897,0.002522172,0.002361012,0.9322023,0.0002409301,0.0002673902,0.01253093],"study_design_scores_gemma":[0.0004807279,0.0006484473,0.001141436,0.00004891949,0.00001481871,0.00004692194,0.001561221,0.8192754,0.16998,0.000009311044,0.006583198,0.0002095154],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315349,0.00005272544,0.06233791,0.000009736915,0.00009730195,0.0001496342,0.000006527223,0.00002699615,0.005784233],"genre_scores_gemma":[0.984849,0.00006340805,0.01382233,0.000120876,0.0005629859,0.000004588201,0.00006730069,0.00001799198,0.0004915659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8169144,"threshold_uncertainty_score":0.4949981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01461999723287225,"score_gpt":0.2846627027470284,"score_spread":0.2700427055141561,"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."}}