{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006356582,0.0007531066,0.00122708,0.001841944,0.0004906163,0.0006460574,0.0009662313,0.000664841,0.001707792],"category_scores_gemma":[0.001115907,0.0001952313,0.0007392981,0.001548584,0.0002161365,0.0005874419,0.0005615887,0.0005907923,0.0008275668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000257401,"about_ca_system_score_gemma":0.0006863251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002459438,"about_ca_topic_score_gemma":0.003374792,"domain_scores_codex":[0.9995281,0.00005469309,0.00003817679,0.0001144483,0.0002050014,0.00005974364],"domain_scores_gemma":[0.9992779,0.0002656043,0.00004784556,0.00005777896,0.0003018185,0.00004896314],"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":[0.000451571,0.0004180346,0.005772128,0.0001005924,0.0001899453,0.000258614,0.00003403905,0.01375153,0.04383093,0.0008464543,0.0143919,0.9199544],"study_design_scores_gemma":[0.0001451891,0.0003014769,0.01027503,0.00001670776,0.0001479647,0.0005420864,0.00004245228,0.9604774,0.02050811,0.002118221,0.00537357,0.00005178817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1097224,0.0009493208,0.8811873,0.0003085915,0.0003121228,0.0001850095,0.001302308,0.00438412,0.001648843],"genre_scores_gemma":[0.5744406,0.0003738449,0.4111362,0.0003844207,0.0003597439,0.0004017254,0.005297094,0.0002794646,0.007326966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002459438,"threshold_uncertainty_score":0.005713105,"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."}}