{"id":"W3008274589","doi":"10.3390/nu12020512","title":"Nutrigenomics and Breast Cancer: State-of-Art, Future Perspectives and Insights for Prevention","year":2020,"lang":"en","type":"review","venue":"Nutrients","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Nutrigenomics; Breast cancer; Genomics; Medicine; Personalized medicine; Proteomics; Exposome; Precision medicine; Bioinformatics; Cancer; Computational biology; Biology; Genome; Environmental health; Genetics; Internal medicine; Pathology","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":[0.001529433,0.0009901535,0.001925918,0.002576376,0.0003810811,0.001886008,0.001199812,0.002065584,0.006623266],"category_scores_gemma":[0.002060339,0.0003263658,0.0007120866,0.002842785,0.0008528319,0.002421652,0.001628133,0.00327225,0.003110232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093475,"about_ca_system_score_gemma":0.001905153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001231593,"about_ca_topic_score_gemma":0.002254176,"domain_scores_codex":[0.9996371,0.0001012953,0.00004580627,0.00005572877,0.0001186146,0.00004154488],"domain_scores_gemma":[0.9986738,0.0007805122,0.0001028309,0.0000362344,0.0002914887,0.0001152355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001087708,0.00007610572,0.0002257143,0.01662576,0.0001232943,0.0001805651,0.00006675399,0.0002229347,0.000678112,0.00765442,0.05086309,0.9231744],"study_design_scores_gemma":[0.00001659961,0.00006382559,0.0006502869,0.007203685,0.0001093901,0.0007348218,0.00008258771,0.00007535741,0.0001472241,0.005868108,0.9850278,0.00002037342],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002711306,0.9985794,0.00006758703,0.0006797799,0.0002545228,0.000001794471,0.00001160962,0.000004838644,0.0003733911],"genre_scores_gemma":[0.0001863695,0.9989896,0.00009283241,0.0002742108,0.0002262467,0.000003047549,0.00001700683,0.000001151153,0.0002094192],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006623266,"threshold_uncertainty_score":0.02215701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187212115206035,"score_gpt":0.2956551850304157,"score_spread":0.2837830638783553,"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."}}