{"id":"W2799888754","doi":"10.1161/circresaha.117.310909","title":"Personal Omics for Precision Health","year":2018,"lang":"en","type":"review","venue":"Circulation Research","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Omics; Computational biology; Biology; Data science; Computer science; Medicine; Bioinformatics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00264425,0.0001925077,0.0004419639,0.0002225142,0.0003135187,0.00007978051,0.0002425978,0.0004434545,0.00001940367],"category_scores_gemma":[0.0005152285,0.0001791862,0.0003146567,0.0002431418,0.00009872995,0.00000270623,0.0001190033,0.0002116584,0.00004615378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001198779,"about_ca_system_score_gemma":0.0009296857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001090555,"about_ca_topic_score_gemma":0.00001462027,"domain_scores_codex":[0.9976782,0.000455958,0.0004487665,0.000612693,0.0003973407,0.0004069953],"domain_scores_gemma":[0.9985631,0.000147363,0.0001966252,0.0004234368,0.0005320344,0.0001374994],"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":[0.00001296486,0.0000320643,0.00001578577,0.002597339,0.00005713574,1.354903e-7,0.00003359027,0.000008615674,0.0001229034,0.00005100033,0.003090207,0.9939783],"study_design_scores_gemma":[0.0001490496,0.0002545071,0.00003677076,0.0005580909,0.00002105669,0.000001730555,0.000008006868,0.0002944496,0.00005550929,0.0002772317,0.9981734,0.0001702442],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000217323,0.989619,0.007928457,0.00004170884,0.0002159704,0.001592248,0.0001097265,0.000007880786,0.0002676836],"genre_scores_gemma":[0.0002929432,0.9938662,0.000652696,0.00001445,0.001262586,0.0002219175,0.002871409,0.00006499097,0.0007528299],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9950832,"threshold_uncertainty_score":0.7307003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.301322436119855,"score_gpt":0.5278792958247109,"score_spread":0.226556859704856,"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."}}