{"id":"W2076311439","doi":"10.2174/187569210793368230","title":"Editorial (Nutriproteomics and Proteogenomics: Cultivating Two Novel Hybrid Fields of Personalized Medicine with Added Societal Value)","year":2010,"lang":"en","type":"article","venue":"Current pharmacogenomics and personalized medicine (Online)/Current pharmacogenomics and personalized medicine","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; U.S. Public Health Service","keywords":"Proteogenomics; Biobank; Human proteome project; Futures studies; Proteome; Personalized medicine; Metabolome; Biotechnology; Value (mathematics); Computational biology; Data science; Biology; Proteomics; Genomics; Bioinformatics; Genome; Computer science; Genetics; Gene; Metabolomics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001544661,0.001397334,0.001836066,0.000416116,0.0006030363,0.00005791565,0.0006126637,0.0003309519,0.0003670677],"category_scores_gemma":[0.0003960065,0.001136329,0.0003074936,0.000328315,0.004519996,0.00006136864,0.0003849686,0.001465637,0.000001064547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008257783,"about_ca_system_score_gemma":0.0006104234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001758488,"about_ca_topic_score_gemma":0.00003019439,"domain_scores_codex":[0.9940538,0.0002404003,0.001679943,0.001908842,0.001018198,0.00109876],"domain_scores_gemma":[0.9956015,0.0002721533,0.00113417,0.0005688654,0.0008582888,0.001565058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005550473,0.0008898191,0.002207668,0.001560469,0.000774403,0.00001085121,0.003520058,0.00001291594,0.9512816,0.0007709756,0.01664325,0.01677752],"study_design_scores_gemma":[0.1204847,0.002867556,0.0003429354,0.001771515,0.004038727,0.0006202843,0.003702924,0.006601593,0.0208744,0.0007710649,0.8352557,0.0026686],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8957074,0.08300387,0.001616781,0.002427123,0.01260748,0.002226561,0.0022691,0.00005580619,0.00008585573],"genre_scores_gemma":[0.6159645,0.2336607,0.004710028,0.002795976,0.1327417,0.0003759542,0.008969019,0.0004620619,0.0003200111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9304072,"threshold_uncertainty_score":0.9998777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011669054472483,"score_gpt":0.3446838011011014,"score_spread":0.3145671105563765,"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."}}