{"id":"W2811297572","doi":"10.1016/j.compbiolchem.2018.06.007","title":"A novel feature selection method to predict protein structural class","year":2018,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Science and Technology Major Project of Guangxi; Natural Sciences and Engineering Research Council of Canada; Ministry of Industry and Information Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Feature (linguistics); Pattern recognition (psychology); Benchmark (surveying); Class (philosophy); Artificial intelligence; Data mining; Feature vector; Projection (relational algebra); Set (abstract data type); Machine learning; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000116275,0.0001270215,0.00009530195,0.00001716662,0.0001378951,0.00001646162,0.0001054013,0.0002345549,0.00003580402],"category_scores_gemma":[0.00009758562,0.0001155754,0.00002862819,0.0000732828,0.000114469,0.000002964204,0.0001017021,0.0001407656,0.000005971568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001043419,"about_ca_system_score_gemma":0.00005495303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002882105,"about_ca_topic_score_gemma":0.000001759079,"domain_scores_codex":[0.9993848,0.00002584678,0.0001252735,0.000251985,0.00005739952,0.0001546997],"domain_scores_gemma":[0.999619,0.00001641669,0.00006523528,0.00009004574,0.0001334005,0.00007591879],"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":[0.00009379082,0.00001080367,0.003808164,0.00003379494,0.00004701556,1.370229e-7,0.00004196893,0.0008125742,0.9906985,0.0003483194,0.001483227,0.002621673],"study_design_scores_gemma":[0.001659182,0.0009939461,0.02680992,0.00004731234,0.00003647655,0.0005763472,0.00006856144,0.09475311,0.7818623,0.002986678,0.08945189,0.0007542854],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7726153,0.00004677922,0.2250128,0.000572608,0.00005352708,0.0001454281,0.00004455413,0.00002873576,0.001480264],"genre_scores_gemma":[0.8335317,6.402553e-7,0.1642511,0.0004992897,0.0004862835,0.00001571926,0.0004253302,0.000008911463,0.0007809844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2088362,"threshold_uncertainty_score":0.4713027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004953738647541233,"score_gpt":0.2895890513252258,"score_spread":0.2846353126776846,"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."}}