{"id":"W2790448184","doi":"10.1101/255257","title":"COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; University of Toronto","funders":"","keywords":"splice; RNA splicing; Computer science; Sequence (biology); Computational biology; Alternative splicing; Selection (genetic algorithm); Artificial intelligence; Site selection; RNA; Biology; Genetics; Gene; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007276138,0.0009295997,0.0005786844,0.0005219292,0.0002418124,0.0005429618,0.001041396,0.001007354,0.002217625],"category_scores_gemma":[0.001310439,0.0003794093,0.000586048,0.0005238441,0.0003616866,0.0007343269,0.0006076864,0.001229825,0.0007716277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000688242,"about_ca_system_score_gemma":0.0008480569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005961314,"about_ca_topic_score_gemma":0.01221614,"domain_scores_codex":[0.9997867,0.00004491785,0.000008343652,0.00007899073,0.00004291744,0.00003822362],"domain_scores_gemma":[0.9995134,0.0002333445,0.00005048349,0.00005452189,0.00009718919,0.00005102385],"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":[0.0004798772,0.0002602688,0.01638137,0.0001351862,0.0001494552,0.0001649208,0.00004121916,0.8512212,0.01920108,0.005583613,0.01328044,0.09310125],"study_design_scores_gemma":[0.000008772864,0.000009747909,0.0002509949,0.000002061778,0.000003375503,0.000008942608,0.000002580423,0.9962897,0.001381984,0.001830456,0.0002085231,0.000002763799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.496513,0.001359433,0.4843699,0.0009044316,0.0001584533,0.00007580007,0.00517021,0.006846673,0.004602055],"genre_scores_gemma":[0.9011117,0.0002192826,0.08460517,0.0004776103,0.00006079307,0.0001000786,0.008515413,0.0002583071,0.004651643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005961314,"threshold_uncertainty_score":0.01185328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307067794190906,"score_gpt":0.2547649250827617,"score_spread":0.2416942471408527,"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."}}