{"id":"W4321605231","doi":"10.1002/sim.9691","title":"Identifying important gene signatures of BMI using network structure‐aided nonparametric quantile regression","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Quantile regression; Computer science; Nonparametric statistics; Penalty method; Regression; Data mining; Machine learning; Mathematics; Statistics; Mathematical optimization","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":[0.001254344,0.0005269339,0.0005656384,0.0008604312,0.0002502693,0.0004899232,0.0006967939,0.0004167649,0.001129425],"category_scores_gemma":[0.004207969,0.0002085245,0.0005973001,0.0008301123,0.0004444238,0.0004857999,0.0006703435,0.0008660157,0.0002197903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000418481,"about_ca_system_score_gemma":0.0006515833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002655963,"about_ca_topic_score_gemma":0.003429609,"domain_scores_codex":[0.9996305,0.0001635944,0.00001073128,0.0001015598,0.00005600075,0.00003763931],"domain_scores_gemma":[0.9986117,0.0008803144,0.0002284049,0.0001163687,0.0001188712,0.00004419461],"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":[0.0002777541,0.0001684387,0.04208027,0.0001814543,0.0002213134,0.000252084,0.0001229628,0.7707848,0.01599557,0.02319213,0.002043424,0.1446799],"study_design_scores_gemma":[0.0000113491,0.00001762797,0.004650477,0.000006363274,0.00001760499,0.00003336636,0.00001411024,0.9828531,0.0008242604,0.01119153,0.0003720264,0.00000822538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09869918,0.0002989453,0.8992323,0.0002945765,0.00001791179,0.00004125387,0.0003877254,0.0002742248,0.0007538252],"genre_scores_gemma":[0.874316,0.0004015724,0.1222495,0.0001382818,0.00005653509,0.0001289708,0.001060773,0.0000655369,0.001582787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002655963,"threshold_uncertainty_score":0.006633699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145187665554891,"score_gpt":0.3235537460499062,"score_spread":0.3021018693943572,"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."}}