{"id":"W2042427414","doi":"10.1145/2494444.2494459","title":"Protein structural class prediction using predicted secondary structure and hydropathy profile","year":2013,"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":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Computer science; Sequence (biology); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Domain (mathematical analysis); Protein secondary structure; Protein structure prediction; Machine learning; Folding (DSP implementation); Data mining; Protein folding; Protein structure; Mathematics; Biology; Engineering","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":[0.0004468019,0.000520153,0.0006480396,0.002773758,0.0003507819,0.0005689006,0.0004664193,0.000516016,0.002489242],"category_scores_gemma":[0.001634425,0.0001444528,0.0004795705,0.001276838,0.0001518964,0.0007237328,0.0003527327,0.0003861923,0.002097363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002500816,"about_ca_system_score_gemma":0.0002714272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001007726,"about_ca_topic_score_gemma":0.001371036,"domain_scores_codex":[0.9997447,0.00002279646,0.00002027292,0.00009957281,0.00008407315,0.00002861515],"domain_scores_gemma":[0.9990793,0.0002410516,0.0002224105,0.0001158742,0.0002535228,0.00008782987],"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":[0.001307873,0.0006572885,0.1964374,0.0005012438,0.0001846245,0.0006159803,0.0001634704,0.01366731,0.1221078,0.00101851,0.01515418,0.6481844],"study_design_scores_gemma":[0.0001047855,0.0006468441,0.2293053,0.00008084984,0.0001614894,0.001916868,0.0002697188,0.6502847,0.09890432,0.004981488,0.01325128,0.00009227321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8678891,0.0008750551,0.1060537,0.0002300819,0.0000861268,0.0001551664,0.009803087,0.00946134,0.005446451],"genre_scores_gemma":[0.9027279,0.0004064594,0.08059433,0.00004528503,0.00004071785,0.00005421412,0.0138611,0.0001362753,0.002133721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002773758,"threshold_uncertainty_score":0.008327305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003555813333943347,"score_gpt":0.2090870783872556,"score_spread":0.2055312650533123,"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."}}