{"id":"W3105461370","doi":"10.1101/445304","title":"A Sparse Additive Model for High-Dimensional Interactions with an Exposure Variable","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; Jewish General Hospital; University of British Columbia; McGill University","funders":"","keywords":"Oracle; Feature selection; Computer science; Linear model; Variable (mathematics); Linear regression; Machine learning; Artificial intelligence; Mathematics","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":[0.008856308,0.001365857,0.002488813,0.001428397,0.000745852,0.001878184,0.003897469,0.002136693,0.004701443],"category_scores_gemma":[0.01794909,0.001234406,0.002125692,0.001816411,0.002289202,0.001196612,0.002075348,0.003406083,0.0009159484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124261,"about_ca_system_score_gemma":0.001347482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01157789,"about_ca_topic_score_gemma":0.01274486,"domain_scores_codex":[0.9965402,0.001955602,0.0001215231,0.0008462596,0.0003218187,0.0002146415],"domain_scores_gemma":[0.9838267,0.01354564,0.00101546,0.0007734743,0.0006035917,0.000235138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004093055,0.0001688307,0.01336384,0.0001778359,0.0003989466,0.0004618543,0.0002066692,0.9054008,0.001580854,0.04232622,0.00252487,0.03298001],"study_design_scores_gemma":[0.00003111676,0.00003304288,0.00130541,0.00001200642,0.00004132835,0.00004344623,0.00001244184,0.9854827,0.0001514854,0.01231254,0.000557849,0.00001666647],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04560346,0.0003544417,0.950996,0.0007353093,0.00005646335,0.00009544866,0.001093269,0.0005660858,0.0004995123],"genre_scores_gemma":[0.7275876,0.0007450207,0.2562477,0.0007418808,0.0003090607,0.000961027,0.003734581,0.0002019221,0.009471253],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01157789,"threshold_uncertainty_score":0.04683721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749127435425286,"score_gpt":0.2402245504641861,"score_spread":0.2227332761099333,"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."}}