{"id":"W3124079586","doi":"10.1109/tcbb.2021.3053181","title":"An Ensemble Hybrid Feature Selection Method for Neuropsychiatric Disorder Classification","year":2021,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hunan Province; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Pattern recognition (psychology); Feature selection; Artificial intelligence; Feature (linguistics); Computer science; Phenomics; Feature extraction; Ensemble learning; Biology; Genomics; Genetics","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":[0.00149521,0.001172339,0.001417745,0.001805876,0.0005678288,0.0005364435,0.001104482,0.0007376008,0.0009228368],"category_scores_gemma":[0.001612788,0.0002748571,0.001226675,0.001380154,0.0002710623,0.0006337723,0.0007037508,0.0006782277,0.0003536734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003779721,"about_ca_system_score_gemma":0.0006797898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00488894,"about_ca_topic_score_gemma":0.005517952,"domain_scores_codex":[0.9992317,0.0001960483,0.00004936008,0.0001678335,0.0002519933,0.0001031955],"domain_scores_gemma":[0.9992908,0.0002453387,0.00005042587,0.00007182452,0.0002992678,0.00004232962],"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":[0.0004635471,0.0002720517,0.01150696,0.00007514335,0.0004300635,0.0002518474,0.00008904867,0.1225944,0.0123467,0.0008112986,0.007107925,0.8440509],"study_design_scores_gemma":[0.00002710315,0.0001350227,0.003933326,0.00001053668,0.00007512983,0.0001686113,0.00002771167,0.9901849,0.00324762,0.0009962439,0.001174726,0.00001910487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1327136,0.001584572,0.8612617,0.0003233606,0.0001508324,0.0001407297,0.0006120434,0.002060287,0.001152848],"genre_scores_gemma":[0.8059481,0.0004405914,0.1879281,0.0002614648,0.0001891241,0.0002612166,0.002214754,0.0001276525,0.002628984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00488894,"threshold_uncertainty_score":0.009720981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03253841907178095,"score_gpt":0.3221496625034103,"score_spread":0.2896112434316293,"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."}}