{"id":"W2951861510","doi":"10.1002/gepi.22112","title":"An analytic approach for interpretable predictive models in high‐dimensional data in the presence of interactions with exposures","year":2018,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; Jewish General Hospital; Université de Sherbrooke; McGill University Health Centre","funders":"Canadian Institutes of Health Research; Ludmer Centre for Neuroinformatics and Mental Health","keywords":"Cluster analysis; Dimensionality reduction; Computer science; Feature selection; Dimension (graph theory); Variable (mathematics); Selection (genetic algorithm); Curse of dimensionality; Data mining; Binary number; Correlation; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007577701,0.00168992,0.001427955,0.002770786,0.0009111696,0.002035098,0.002751251,0.001574486,0.003900399],"category_scores_gemma":[0.03006068,0.001039636,0.002086138,0.002135472,0.002203208,0.002368827,0.002658803,0.004037893,0.0007888629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001733742,"about_ca_system_score_gemma":0.002353289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006769723,"about_ca_topic_score_gemma":0.005528963,"domain_scores_codex":[0.9970686,0.001866987,0.0001091181,0.0003726616,0.0004810336,0.0001015987],"domain_scores_gemma":[0.9799172,0.01721113,0.00111609,0.0008182225,0.0007563498,0.0001809901],"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":[0.00004899132,0.00005794231,0.00252868,0.0002231215,0.0002745496,0.0003408368,0.0003132057,0.657451,0.0008323755,0.2947836,0.004830302,0.03831543],"study_design_scores_gemma":[0.00000782561,0.00001363281,0.000245738,0.00003283115,0.00002143425,0.00004208517,0.00002044147,0.8360878,0.0001386552,0.1615746,0.001800667,0.00001418526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002022526,0.000227534,0.9959804,0.0007890616,0.00002461869,0.00002453253,0.00017941,0.0002400327,0.0005118918],"genre_scores_gemma":[0.258409,0.001618562,0.7331402,0.0007882618,0.0004611343,0.0008382745,0.001105055,0.0002661851,0.003373399],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007577701,"threshold_uncertainty_score":0.04007518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04435830121658021,"score_gpt":0.3168484521232434,"score_spread":0.2724901509066632,"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."}}