{"id":"W4242991188","doi":"10.32920/ryerson.14657172.v1","title":"Protein structural class prediction using predicted secondary structure and hydropathy profile","year":2021,"lang":"en","type":"preprint","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; Artificial intelligence; Class (philosophy); Computer science; Machine learning; Feature (linguistics); Sequence (biology); Pattern recognition (psychology); Protein structure prediction; Data mining; Protein folding; Folding (DSP implementation); Protein structure; Engineering; Biology","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":[0.0004354597,0.0004217495,0.0005490803,0.001627865,0.0003361318,0.0007001401,0.0004883333,0.0004467521,0.002262315],"category_scores_gemma":[0.001719936,0.0001585599,0.0004509262,0.000996732,0.0001297722,0.0008418013,0.0003601151,0.0003775513,0.001989821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002696642,"about_ca_system_score_gemma":0.0002984927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008296777,"about_ca_topic_score_gemma":0.001058543,"domain_scores_codex":[0.9997527,0.00002965228,0.00001638433,0.00008630076,0.00008925831,0.00002577557],"domain_scores_gemma":[0.9993322,0.0002114091,0.0001515434,0.0001087387,0.0001504975,0.00004563466],"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":[0.0008683176,0.0005778299,0.1275325,0.0003719822,0.0001698447,0.0004422532,0.0001815857,0.03227792,0.1115213,0.002512529,0.01129836,0.7122456],"study_design_scores_gemma":[0.00005658719,0.000370509,0.07062519,0.00007169779,0.00007861143,0.0008313112,0.000170337,0.821318,0.0869673,0.007118092,0.01233803,0.00005438207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8215477,0.001153396,0.1599648,0.0003222302,0.0000809309,0.0000760678,0.005957224,0.005794112,0.005103582],"genre_scores_gemma":[0.8862939,0.0005670549,0.099049,0.00004354545,0.00004366992,0.00003888902,0.01144853,0.0001269763,0.002388389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002262315,"threshold_uncertainty_score":0.00756824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005863915885939899,"score_gpt":0.2344738505864852,"score_spread":0.2286099347005453,"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."}}