{"id":"W4385189302","doi":"10.2197/ipsjtbio.16.20","title":"AtLASS: A Scheme for End-to-End Prediction of Splice Sites Using Attention-based Bi-LSTM","year":2023,"lang":"en","type":"article","venue":"IPSJ Transactions on Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science","keywords":"Computer science; Exploit; splice; Annotation; False positive paradox; Genome; RNA splicing; Artificial intelligence; Intron; Exon; Computational biology; Machine learning; Data mining; RNA; Gene; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080127,0.001203061,0.0007329852,0.00102991,0.0004646256,0.0005812199,0.001868203,0.001141139,0.003872697],"category_scores_gemma":[0.001386951,0.0004318002,0.0007468886,0.0008541592,0.0003909425,0.00141254,0.001167185,0.001453041,0.002047038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007453233,"about_ca_system_score_gemma":0.001306615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005651188,"about_ca_topic_score_gemma":0.009657767,"domain_scores_codex":[0.9996878,0.00004663353,0.00002386603,0.0001283501,0.0000693007,0.00004409085],"domain_scores_gemma":[0.9996402,0.0001187302,0.00003724856,0.00006622843,0.0001035174,0.00003396533],"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":[0.000707151,0.0001911989,0.002538903,0.0002687982,0.0001817843,0.0002287845,0.0001610649,0.09161923,0.05805583,0.006499031,0.01793454,0.8216137],"study_design_scores_gemma":[0.00002299215,0.00005892122,0.0004777916,0.00001282667,0.00002892758,0.00005205764,0.00001878047,0.9786283,0.01244435,0.00585132,0.002385191,0.00001850387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01709784,0.000624279,0.9645791,0.0001714233,0.0001878663,0.0001153253,0.0008840157,0.01467748,0.001662647],"genre_scores_gemma":[0.3050762,0.0005864049,0.6804957,0.0005466351,0.0001524324,0.0004643199,0.004340224,0.0006235476,0.007714563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005651188,"threshold_uncertainty_score":0.01295543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0340141982999583,"score_gpt":0.270384650472266,"score_spread":0.2363704521723077,"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."}}