{"id":"W4405390835","doi":"10.1016/j.compbiomed.2024.109567","title":"Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene selection","year":2024,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Subspace topology; Artificial intelligence; Computer science; Machine learning; Feature learning; Unsupervised learning; Representation (politics); Perturbation (astronomy); Rank (graph theory); Mathematics; Pattern recognition (psychology); Combinatorics; Physics","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":[0.0002345506,0.00009105449,0.0001055578,0.0001125438,0.00008991601,0.00001366574,0.00003069489,0.0001242094,0.000007830434],"category_scores_gemma":[0.00005971785,0.00007435061,0.00001447119,0.0001373983,0.000107073,0.000006879384,0.00003498969,0.0001458499,4.168614e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001667066,"about_ca_system_score_gemma":0.00002031575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001728959,"about_ca_topic_score_gemma":0.00000361453,"domain_scores_codex":[0.9992678,0.0001270336,0.0001345212,0.000322944,0.00003914633,0.0001085997],"domain_scores_gemma":[0.9998073,0.00003480682,0.00003676851,0.0000523324,0.00003023099,0.00003856821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006389867,0.000008202417,0.0311574,0.00003397522,0.00001909535,0.000001523445,0.00048932,0.0005404914,0.946273,0.0007226405,0.0001077919,0.02058261],"study_design_scores_gemma":[0.0059734,0.00263016,0.1076412,0.001365629,0.0001717533,0.000401709,0.002771533,0.6462212,0.1902989,0.006717685,0.03476414,0.001042681],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421536,0.004841814,0.05182827,0.0006192264,0.0003287093,0.0000999239,2.322597e-7,0.00002357154,0.0001046561],"genre_scores_gemma":[0.9956807,0.002924871,0.0006940063,0.0001209968,0.0002615142,0.00000665466,0.00009613546,0.000008710776,0.0002064493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7559742,"threshold_uncertainty_score":0.303193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01873947806517249,"score_gpt":0.3265312530407453,"score_spread":0.3077917749755728,"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."}}