{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005810011,0.004245875,0.003836851,0.003998821,0.002332706,0.006097597,0.003670397,0.007377099,0.01598897],"category_scores_gemma":[0.01829678,0.0010212,0.002319722,0.001745996,0.001834448,0.004028837,0.001085613,0.01203424,0.01909429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001592372,"about_ca_system_score_gemma":0.00173412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000780864,"about_ca_topic_score_gemma":0.001879443,"domain_scores_codex":[0.9965498,0.0006035146,0.0004308267,0.000585647,0.001620318,0.0002098439],"domain_scores_gemma":[0.9793314,0.007773235,0.001057722,0.0004147253,0.008585826,0.00283715],"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":[0.00004070968,0.0000120703,0.0000228255,0.0001540458,0.00001569771,0.0000519374,0.000004665939,0.00003518694,0.00004970726,0.0001818155,0.9939734,0.005457818],"study_design_scores_gemma":[0.00007657792,0.00005732238,0.0003737597,0.0003237348,0.00006285463,0.0003525679,0.0000242888,0.0005276277,0.0002171907,0.001104027,0.9968566,0.00002349851],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00004300774,0.0063163,0.0003679774,0.02328943,0.9686126,0.00002365664,0.00007118313,0.00009545444,0.00118047],"genre_scores_gemma":[0.000445155,0.005525322,0.0002355814,0.01101048,0.9759846,0.00002212839,0.00005420786,0.0000591724,0.006663323],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01598897,"threshold_uncertainty_score":0.05348843,"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."}}