{"id":"W71331043","doi":"10.2174/138920311796957621","title":"Editorial [Hot Topic: Machine Learning Models in Protein Bioinformatics (Guest Editors: Lukasz Kurgan & Yaoqi Zhou)]","year":2011,"lang":"en","type":"editorial","venue":"Current Protein and Peptide Science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Automatic summarization; Computational biology; Structural bioinformatics; Computer science; Bioinformatics; Field (mathematics); Function (biology); Data science; Artificial intelligence; Protein structure; Biology; Genetics; Biochemistry","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002388589,0.0007405754,0.0005955159,0.0004207355,0.0004605821,0.0004101803,0.001380241,0.001009381,0.00003351742],"category_scores_gemma":[0.003384767,0.0006726429,0.0001420458,0.0004927136,0.0008471399,0.0001362354,0.001093963,0.002564848,0.00004723907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001173742,"about_ca_system_score_gemma":0.001188945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002546694,"about_ca_topic_score_gemma":0.0001463627,"domain_scores_codex":[0.9952243,0.0001668668,0.001060448,0.0009344656,0.001633858,0.0009800903],"domain_scores_gemma":[0.9973732,0.00005812285,0.0007698414,0.0008768437,0.0005467575,0.0003751916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000185366,0.0001427005,0.0001521108,0.00116542,0.00002303502,0.000001480988,0.0009753376,0.0001548282,0.005975449,0.0001217765,0.9869591,0.004143377],"study_design_scores_gemma":[0.0007909226,0.0003915333,0.000008075642,0.000723335,0.00001799552,0.000003031364,0.00003335123,0.002597623,0.001702189,0.0001449726,0.9927491,0.00083787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.003537237,0.0008688011,0.001504874,0.00003489059,0.990203,0.001598808,0.0001303227,0.0000823714,0.002039653],"genre_scores_gemma":[0.001194792,0.0004778178,0.005413266,0.00001071333,0.9896901,0.0002101103,0.0009885582,0.00006741048,0.001947204],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.005789981,"threshold_uncertainty_score":0.9997362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914257167345022,"score_gpt":0.2773190226497393,"score_spread":0.2581764509762891,"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."}}