{"id":"W1988280752","doi":"10.1109/bibmw.2012.6470326","title":"Protein secondary structure prediction using support vector machines and a codon encoding scheme","year":2012,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Encoding (memory); Computer science; Artificial intelligence; Curse of dimensionality; Sequence (biology); Synonymous substitution; Support vector machine; Binary number; Codon usage bias; Computation; Algorithm; Pattern recognition (psychology); Computational biology; Gene; Mathematics; Genetics; Biology; Genome; Arithmetic","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":[],"consensus_categories":[],"category_scores_codex":[0.0001655193,0.0001337025,0.00009524674,0.00003456588,0.00009566006,0.00002850741,0.00006502782,0.0001413916,0.0002359618],"category_scores_gemma":[0.00007495176,0.0001132583,0.00002791435,0.00004182109,0.00004081265,0.00001666738,0.0001081866,0.0001491113,0.000002768527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001138066,"about_ca_system_score_gemma":0.00003647367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000165619,"about_ca_topic_score_gemma":0.000006803877,"domain_scores_codex":[0.9993343,0.00002906273,0.0001898952,0.0001311505,0.00009446995,0.0002210622],"domain_scores_gemma":[0.9996307,0.000003898296,0.00008564608,0.0001612506,0.00002931257,0.00008915634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001948407,0.00001120643,0.06842547,0.00008146052,0.00002519557,2.56674e-7,0.0001301308,0.00001312611,0.929597,0.0001761252,0.0003164258,0.001204158],"study_design_scores_gemma":[0.001813898,0.0007101588,0.07438195,0.00007467721,0.00007671818,0.0006337736,0.0002935478,0.05021795,0.7957668,0.00008288639,0.07496364,0.0009840133],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939982,0.0001495091,0.002151783,0.00003501373,0.0001359224,0.000197596,0.00003982009,0.00002892516,0.003263289],"genre_scores_gemma":[0.9740435,0.0000062336,0.02477954,0.0001375247,0.0003431006,0.000004140791,0.0001595969,0.00001721657,0.0005091119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1338302,"threshold_uncertainty_score":0.461854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008860714384948417,"score_gpt":0.2498142312872703,"score_spread":0.2409535169023218,"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."}}