{"id":"W2809990446","doi":"10.1093/bioinformatics/bty537","title":"Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Transport Canada","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health; NOMIS Stiftung; Wellcome Trust; Ovarian Cancer Research Fund; National Institute of Allergy and Infectious Diseases; Bill and Melinda Gates Foundation","keywords":"Metabolome; Transcriptome; Proteome; Biology; Computational biology; Microbiome; Pregnancy; Bioinformatics; Physiology; Metabolomics; Genetics; Gene; Gene expression","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":[],"consensus_categories":[],"category_scores_codex":[0.00008896226,0.0001527116,0.0002825029,0.0001171687,0.0003634092,0.00001657161,0.0001333072,0.00007743014,0.000009031999],"category_scores_gemma":[0.00003892795,0.0001057126,0.00009685992,0.0002523895,0.0002817993,0.0002165575,0.00008718354,0.0001344963,0.000005051552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002175879,"about_ca_system_score_gemma":0.00004422542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001866994,"about_ca_topic_score_gemma":0.00001572539,"domain_scores_codex":[0.998976,0.00001694127,0.000549195,0.00009948164,0.0001474078,0.000211],"domain_scores_gemma":[0.9992794,0.00001401077,0.0001680103,0.0003444127,0.0001495484,0.00004463709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003346747,0.0005720853,0.02860609,0.008674548,0.001655212,0.000004808772,0.1044912,0.0002757809,0.8430883,0.003468896,0.0001161656,0.008712227],"study_design_scores_gemma":[0.01669477,0.001204546,0.6477885,0.01386028,0.001853883,0.0003899846,0.009290691,0.1902314,0.1138336,0.001440166,0.001810794,0.001601409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899735,0.002380847,0.003214777,0.0001259367,0.0001407902,0.0009625108,0.00005463346,0.00005483725,0.003092094],"genre_scores_gemma":[0.9785922,0.0003110604,0.02072119,0.0000425874,0.0000359814,0.00003462646,0.000006636628,0.00001377616,0.0002419102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7292548,"threshold_uncertainty_score":0.4310834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02945929803773082,"score_gpt":0.2623782766042967,"score_spread":0.2329189785665659,"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."}}