{"id":"W2549940642","doi":"10.1016/j.ecosta.2016.10.004","title":"Identifying gene-environment interactions for prognosis using a robust approach","year":2016,"lang":"en","type":"article","venue":"Econometrics and Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"National Cancer Institute","keywords":"Coordinate descent; Computer science; Accelerated failure time model; Quantile; Mixture model; Consistency (knowledge bases); Expectation–maximization algorithm; Stability (learning theory); Data mining; Quantile regression; Statistics; Mathematics; Algorithm; Covariate; Machine learning; Artificial intelligence; Maximum likelihood","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":[0.01503185,0.001117005,0.00311386,0.002400457,0.0005607338,0.001636257,0.002187523,0.001788016,0.002590353],"category_scores_gemma":[0.03456533,0.0008894395,0.00343617,0.001641452,0.00128138,0.001254002,0.001591199,0.001976524,0.0007682179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007168504,"about_ca_system_score_gemma":0.002011299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004924557,"about_ca_topic_score_gemma":0.004083898,"domain_scores_codex":[0.9933962,0.004353871,0.0003312896,0.001043242,0.0005368533,0.0003385885],"domain_scores_gemma":[0.9699093,0.0252178,0.001695711,0.002129296,0.0007078599,0.0003399898],"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":[0.0009741472,0.0004966374,0.04012526,0.0002083913,0.003470666,0.000557599,0.0001021812,0.7413832,0.005794893,0.02339183,0.002987157,0.180508],"study_design_scores_gemma":[0.00006299563,0.0001697243,0.005285802,0.00001176282,0.0002393327,0.00008106059,0.0000231104,0.972693,0.0006779147,0.02027001,0.0004524095,0.00003286203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02640875,0.0004191274,0.9715419,0.0005687242,0.00003586336,0.00004757675,0.0003226548,0.0004384393,0.0002168532],"genre_scores_gemma":[0.7896127,0.0004556342,0.2053139,0.0003851363,0.0002731621,0.0002191568,0.001394375,0.0001883748,0.002157504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01503185,"threshold_uncertainty_score":0.07949692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127961068007003,"score_gpt":0.2945561317281314,"score_spread":0.1817600249274312,"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."}}