{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000187671,0.0001748867,0.0001560638,0.0002019219,0.0006812083,0.00008744854,0.00013542,0.0001373253,0.00002776796],"category_scores_gemma":[0.0001161118,0.00016577,0.00008502232,0.000482485,0.00008299024,0.0002759632,0.00000277832,0.000262257,0.00002152892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003587664,"about_ca_system_score_gemma":0.0001100581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002056435,"about_ca_topic_score_gemma":0.00001127398,"domain_scores_codex":[0.998744,0.0002196314,0.0003246456,0.0003786521,0.0001328037,0.0002002342],"domain_scores_gemma":[0.9986503,0.000690631,0.0001611162,0.0002178087,0.0001876167,0.00009251772],"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":[0.000341941,0.0008241212,0.0001798914,0.0001366004,0.00004750253,0.000001605966,0.0003925602,0.04355513,0.1658315,0.01635846,0.000697368,0.7716333],"study_design_scores_gemma":[0.0008598397,0.0004423047,0.001914121,0.00000754468,0.00004292949,0.0003086565,0.0002144012,0.9256532,0.05199052,0.01359759,0.004709058,0.0002598012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02519657,0.0000150922,0.9705589,0.002849974,0.0006223767,0.0003118541,0.0001441638,0.000149414,0.0001516003],"genre_scores_gemma":[0.7812214,0.00006538796,0.2159764,0.002173857,0.00008468837,0.00008119405,0.0001570238,0.00001711661,0.0002228821],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8820981,"threshold_uncertainty_score":0.6759903,"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."}}