{"id":"W4211087240","doi":"10.1016/j.isci.2022.103904","title":"Mining RNAseq data reveals dynamic metaboloepigenetic profiles in human, mouse and bovine pre-implantation embryos","year":2022,"lang":"en","type":"article","venue":"iScience","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Fundação de Amparo à Pesquisa do Estado de São Paulo; Alexander von Humboldt-Stiftung","keywords":"Reprogramming; Embryo; Biology; In vitro; Offspring; In vitro fertilisation; Transcriptome; DNA methylation; Embryonic stem cell; Cell biology; Andrology; Epigenetics; Genetics; Gene; Pregnancy; Gene expression; Medicine","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":[0.000877197,0.0002750616,0.0003807654,0.001174492,0.0002195953,0.0008031058,0.0002329847,0.0003328171,0.002122891],"category_scores_gemma":[0.001812833,0.0001308912,0.0003582858,0.001237696,0.000219381,0.0002591271,0.0004014726,0.000451833,0.0007291811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002584646,"about_ca_system_score_gemma":0.0003353944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001699683,"about_ca_topic_score_gemma":0.003905515,"domain_scores_codex":[0.9995535,0.0000849265,0.00003709089,0.0001751248,0.0001037317,0.00004553607],"domain_scores_gemma":[0.9989624,0.0005004246,0.0002332228,0.0001105507,0.0001393773,0.00005409315],"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":[0.0009077503,0.00005062503,0.1971182,0.0004960073,0.0004369507,0.000430097,0.0003446004,0.001918876,0.746514,0.0007819382,0.002075505,0.04892547],"study_design_scores_gemma":[0.00004922034,0.0004771455,0.7516418,0.00008725516,0.0004128387,0.002273225,0.0006489082,0.01248016,0.2021104,0.003753904,0.02597434,0.00009084271],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422386,0.003114913,0.01818394,0.0005786411,0.00003149471,0.00002754804,0.03362921,0.0004271658,0.001768579],"genre_scores_gemma":[0.9485989,0.001293163,0.01427381,0.0002340956,0.00002620718,0.00005062118,0.03334656,0.0001092228,0.002067502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002122891,"threshold_uncertainty_score":0.007101715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02401575235718883,"score_gpt":0.3105103598235344,"score_spread":0.2864946074663455,"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."}}