{"id":"W4413011538","doi":"10.1016/j.ab.2025.115952","title":"K-SNOpred: Identification of protein S-nitrosylation sites through word embedding features and machine learning","year":2025,"lang":"en","type":"article","venue":"Analytical Biochemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University","keywords":"Identification (biology); Computer science; Computational biology; Word embedding; Word (group theory); S-Nitrosylation; Artificial intelligence; Embedding; Chemistry; Natural language processing; Biology; Biochemistry; Botany; Cysteine; Linguistics; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007653655,0.001189829,0.0007496972,0.002055781,0.00038569,0.0006987519,0.001075731,0.0008159598,0.001352716],"category_scores_gemma":[0.001727618,0.0002862885,0.001213545,0.001102216,0.0003192291,0.001205729,0.0008986951,0.001105549,0.001216748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005300746,"about_ca_system_score_gemma":0.0009939806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005299155,"about_ca_topic_score_gemma":0.00720542,"domain_scores_codex":[0.999584,0.00008904636,0.00005114638,0.0001348604,0.00008747808,0.00005351101],"domain_scores_gemma":[0.9994343,0.0002485499,0.00007328652,0.00006096846,0.0001538887,0.00002900281],"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":[0.001037016,0.0009149089,0.04544254,0.0007009477,0.000571946,0.0006958219,0.0002747388,0.1407161,0.02347945,0.004208786,0.02160472,0.760353],"study_design_scores_gemma":[0.00003707961,0.0001894592,0.003929483,0.00002927825,0.0000681876,0.0002541051,0.00006830977,0.9795822,0.007806684,0.003788128,0.00421164,0.00003546471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3647411,0.004415614,0.6019932,0.001331952,0.0004575489,0.0004348025,0.009434019,0.01232477,0.004866955],"genre_scores_gemma":[0.7776001,0.00121581,0.200434,0.0003541165,0.0001301191,0.0004322874,0.01411296,0.0002001789,0.005520332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005299155,"threshold_uncertainty_score":0.01053661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00726323398288616,"score_gpt":0.2821780057269572,"score_spread":0.274914771744071,"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."}}