{"id":"W4388336842","doi":"10.1016/j.compbiomed.2023.107659","title":"Dual regularized subspace learning using adaptive graph learning and rank constraint: Unsupervised feature selection on gene expression microarray datasets","year":2023,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Feature selection; Pattern recognition (psychology); Artificial intelligence; Discriminative model; Feature learning; Computer science; Dimensionality reduction; Matrix decomposition; Non-negative matrix factorization; Cluster analysis; Curse of dimensionality; Machine learning","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":[0.002548983,0.001087702,0.002273204,0.00138752,0.0006739882,0.001072151,0.00168621,0.001000345,0.0009364265],"category_scores_gemma":[0.005724093,0.0004611392,0.001625578,0.002072555,0.001001124,0.001201371,0.001344109,0.001684951,0.0003909017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006420632,"about_ca_system_score_gemma":0.001875627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005701824,"about_ca_topic_score_gemma":0.00748634,"domain_scores_codex":[0.9983373,0.0008891821,0.00006900871,0.0003175197,0.0002344508,0.0001525203],"domain_scores_gemma":[0.9975049,0.001401194,0.0001805655,0.0003791588,0.0004188512,0.0001154793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001358394,0.0008442404,0.004139983,0.0003328512,0.0004343125,0.0001553821,0.0001945751,0.5083475,0.01620013,0.01011726,0.01473681,0.4431387],"study_design_scores_gemma":[0.00002666729,0.00004805187,0.0003460617,0.000002838283,0.00001278463,0.00001458045,0.00001373178,0.9947128,0.0009595126,0.003611675,0.0002426673,0.000008647905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1199068,0.000566944,0.8758793,0.0005087402,0.00005644712,0.0001084742,0.0007191643,0.00164105,0.0006130758],"genre_scores_gemma":[0.5844296,0.0003223618,0.4067471,0.0002405766,0.0001146899,0.0003619168,0.004897105,0.000373144,0.002513489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005701824,"threshold_uncertainty_score":0.01348042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01963936747486579,"score_gpt":0.3040742032820113,"score_spread":0.2844348358071455,"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."}}