{"id":"W2133950045","doi":"10.1186/1559-0275-11-45","title":"Integration of omics sciences to advance biology and medicine","year":2014,"lang":"en","type":"article","venue":"Clinical Proteomics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Cancer Institute; Perelman School of Medicine, University of Pennsylvania; National Institutes of Health; Seoul National University; University of Pennsylvania; Génome Québec; College of Medicine, Seoul National University; University of Texas Southwestern Medical Center; Johns Hopkins University; National Heart, Lung, and Blood Institute; Boise State University; School of Medicine, Boston University; Korea Institute of Science and Technology; Wellcome Trust","keywords":"Omics; Systems biology; Proteomics; Systems medicine; Data integration; Limiting; Precision medicine; Computational biology; Data science; Bioinformatics; Biology; Medicine; Computer science; Pathology; Engineering; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009898432,0.00007193402,0.0001726055,0.000022234,0.00004356318,0.000004807497,0.0001402039,0.0001333478,0.000002130001],"category_scores_gemma":[0.0008635723,0.0000541403,0.0000294446,0.00005314958,0.0004455445,0.000002254445,0.0001020917,0.00007727599,0.000002219938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002310201,"about_ca_system_score_gemma":0.00002322353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005060801,"about_ca_topic_score_gemma":0.000009300003,"domain_scores_codex":[0.9992333,0.00004627726,0.0003931058,0.0001841956,0.00003428504,0.0001088046],"domain_scores_gemma":[0.9995068,0.00007263259,0.0001318032,0.0001694072,0.00005024928,0.00006904986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002626167,0.00006724615,0.009394857,0.00004375912,0.00003527099,8.618568e-8,0.0001233808,0.0003808741,0.6684669,0.05382165,0.001847563,0.2655557],"study_design_scores_gemma":[0.008020798,0.03269742,0.02576448,0.0004417018,0.0001348891,0.00002809931,0.0005904944,0.1018932,0.2603452,0.3090425,0.2591672,0.001874148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6728098,0.0001438417,0.3245398,0.0008018917,0.0002197536,0.0002234896,0.000004691276,0.000003797926,0.001252899],"genre_scores_gemma":[0.9390315,0.0002877235,0.0591034,0.001155436,0.0003475515,0.000006672597,0.00001778349,0.000005308059,0.00004458762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4081218,"threshold_uncertainty_score":0.2207778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969669936545722,"score_gpt":0.3725294178189829,"score_spread":0.3428327184535257,"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."}}