{"id":"W4410087515","doi":"10.1109/wi-iat62293.2024.00126","title":"Protein Sequence Prediction Based on Feature Combination and Attention Mechanism","year":2024,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Mechanism (biology); Sequence (biology); Feature (linguistics); Artificial intelligence; Protein sequencing; Computational biology; Pattern recognition (psychology); Peptide sequence; Chemistry; Biology; Biochemistry; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001385378,0.00007393221,0.00003692952,0.00004156322,0.00004497563,0.00005255101,0.00003674406,0.0001060407,0.00001355721],"category_scores_gemma":[0.00003370868,0.00006191877,0.00002210041,0.00005044997,0.00001628701,0.000004948064,0.00001936303,0.0000987542,0.00001044127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001180979,"about_ca_system_score_gemma":0.00001953398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000274609,"about_ca_topic_score_gemma":0.000001683483,"domain_scores_codex":[0.999588,0.00002121999,0.00007185955,0.000146737,0.0001000109,0.00007222406],"domain_scores_gemma":[0.9998158,0.000003819132,0.00001982548,0.0001103737,0.00002557045,0.00002464815],"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":[0.00002827397,0.00002898178,0.0003922811,0.0001945149,0.00001638723,0.000001544786,0.00002497202,0.0001448239,0.9705983,0.02074169,0.002132209,0.005696044],"study_design_scores_gemma":[0.0004995935,0.001133421,0.002542936,0.0001783011,0.00001936174,0.0000217036,0.000031523,0.8155994,0.1633464,0.001383633,0.0150299,0.0002138146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8557847,0.00009275263,0.12932,0.002666667,0.0003182462,0.0006383436,0.00003249201,0.0001855836,0.01096125],"genre_scores_gemma":[0.993242,0.000007459083,0.003002071,0.0001949789,0.00004844323,0.00002102298,0.0002675884,0.000009323033,0.003207101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8154546,"threshold_uncertainty_score":0.2524974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006211221312361955,"score_gpt":0.2416613159760459,"score_spread":0.2354500946636839,"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."}}