{"id":"W2034088007","doi":"10.1371/journal.pone.0063145","title":"A Consistency-Based Feature Selection Method Allied with Linear SVMs for HIV-1 Protease Cleavage Site Prediction","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Feature selection; Support vector machine; Artificial intelligence; Computer science; Machine learning; Data mining; Dimensionality reduction; Pattern recognition (psychology); Curse of dimensionality; Feature (linguistics); Feature vector","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":[0.0001797974,0.0001637603,0.0001642444,0.00004461898,0.000117184,0.00003564631,0.0000869487,0.0001926518,0.00003186437],"category_scores_gemma":[0.0002061092,0.0001376432,0.0000539869,0.00009047014,0.00003755327,0.000009165558,0.00002342926,0.0001826507,0.00003452492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001839789,"about_ca_system_score_gemma":0.00008462762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001416436,"about_ca_topic_score_gemma":0.00001005,"domain_scores_codex":[0.9991218,0.00006501788,0.000174684,0.0002446232,0.000179375,0.0002144646],"domain_scores_gemma":[0.9992561,0.00002570077,0.0001420502,0.0002484223,0.0002420798,0.00008560194],"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":[0.0005604025,0.0006497405,0.01801977,0.0005915546,0.0003504529,5.007981e-7,0.0001079051,0.001328692,0.9699817,0.00001991367,0.007654958,0.0007344111],"study_design_scores_gemma":[0.002069408,0.001922506,0.003454899,0.0001220176,0.0002337531,0.000008753263,0.00003225584,0.263071,0.7247496,0.00001672539,0.004003669,0.0003154186],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5647444,0.00006453018,0.4279809,0.001582739,0.00003653364,0.003772849,0.0001851354,0.0001737461,0.001459163],"genre_scores_gemma":[0.1742084,0.000006859007,0.8195631,0.0007456145,0.0003081688,0.0007226006,0.001385978,0.00005355405,0.003005665],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3915822,"threshold_uncertainty_score":0.5612928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130240615747399,"score_gpt":0.2344278266892698,"score_spread":0.2214037651145299,"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."}}