{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001956083,0.0006595115,0.0005419279,0.0005134008,0.0003389618,0.0008337807,0.000858813,0.0007458779,0.001117994],"category_scores_gemma":[0.003636052,0.0003529544,0.001441471,0.0006997805,0.0003900035,0.000479075,0.0007698301,0.0008230673,0.0002093115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005832124,"about_ca_system_score_gemma":0.0007729181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063171,"about_ca_topic_score_gemma":0.00638029,"domain_scores_codex":[0.9996729,0.0001603823,0.00001442692,0.00009776894,0.00002396697,0.00003055729],"domain_scores_gemma":[0.9990131,0.000697593,0.00009715156,0.00007589182,0.00006429532,0.00005196936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007149234,0.0001295556,0.06803073,0.0001533238,0.0004331283,0.0003475951,0.0001757273,0.8994982,0.004727787,0.002432593,0.002476875,0.02087955],"study_design_scores_gemma":[0.00001600791,0.00003991909,0.01281374,0.00001576316,0.0000440169,0.00006840115,0.00003342822,0.9824326,0.0007278908,0.003354149,0.0004391768,0.00001478891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7950259,0.00132298,0.1894829,0.001875355,0.00007789054,0.0001023169,0.009770548,0.0006489897,0.001693156],"genre_scores_gemma":[0.9574789,0.0004589116,0.0339997,0.0002226746,0.00004954168,0.0002112993,0.006319812,0.00007177157,0.001187413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01063171,"threshold_uncertainty_score":0.02113968,"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."}}