{"id":"W2333255801","doi":"10.1586/14737159.2016.1164601","title":"Omics for personalized medicine: defining the current we swim in","year":2016,"lang":"en","type":"editorial","venue":"Expert Review of Molecular Diagnostics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Kidney Foundation of Canada","keywords":"Personalized medicine; Omics; Precision medicine; Computational biology; Current (fluid); Medicine; Biology; Bioinformatics; Data science; Computer science; Pathology; Engineering","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005889896,0.0004393576,0.0007891477,0.00006296673,0.00005699845,0.00001260626,0.0007094188,0.0003481935,0.00001640097],"category_scores_gemma":[0.01112649,0.000274027,0.0004829679,0.00009340851,0.0002404805,0.000001931214,0.0002159489,0.0002616961,0.000004802085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004396164,"about_ca_system_score_gemma":0.0005900892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001036455,"about_ca_topic_score_gemma":0.000008677606,"domain_scores_codex":[0.9976861,0.0001728623,0.0007688053,0.0005667361,0.0004476692,0.0003577563],"domain_scores_gemma":[0.9972982,0.000897078,0.0004844946,0.0007824328,0.0004322639,0.0001055716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003970731,0.00006760327,0.000007045316,0.003370917,0.00008722773,0.000008189048,0.00003894161,9.560816e-7,0.001228047,0.0004293397,0.9765289,0.01819307],"study_design_scores_gemma":[0.0008417707,0.0001815808,0.000002913581,0.01558133,0.000163747,0.000002220273,0.00001604267,0.000001591277,0.001121235,0.0002158859,0.9815275,0.000344179],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002806366,0.8295505,0.001546981,0.001191027,0.1659923,0.0009037587,0.000684004,0.000004644942,0.0000987332],"genre_scores_gemma":[0.00008647075,0.8672138,0.0003538028,0.0005970532,0.1290377,0.0004208668,0.002160839,0.00008182329,0.00004760186],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03766334,"threshold_uncertainty_score":0.9999712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008416508778416255,"score_gpt":0.3305098495917143,"score_spread":0.322093340813298,"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."}}