{"id":"W3009482823","doi":"10.1101/2020.03.09.979054","title":"<i>In Vivo</i> Transcriptomic Profiling using Cell Encapsulation Identifies Effector Pathways of Systemic Aging","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; Stem Cell Network; Natural Sciences and Engineering Research Council of Canada; Stiftung für die Erforschung der Muskelkrankheiten; Université de Sherbrooke","keywords":"Transcriptome; In vivo; Effector; Cell biology; Biology; Senescence; Transcription factor; Progenitor cell; In vitro; Gene expression profiling; Computational biology; Stem cell; Gene expression; Gene; Genetics","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":[0.0002588358,0.0002558532,0.0002556557,0.0002240072,0.0001112095,0.0004118958,0.0001091086,0.0002347277,0.0009953072],"category_scores_gemma":[0.000120047,0.0001080795,0.0002021165,0.0001813885,0.000290972,0.0002421492,0.000231433,0.0004064376,0.0004766292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829836,"about_ca_system_score_gemma":0.0001237162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002593024,"about_ca_topic_score_gemma":0.000343607,"domain_scores_codex":[0.9998751,0.000014834,0.00001044453,0.00004524027,0.00003316367,0.00002120708],"domain_scores_gemma":[0.9998796,0.0000223099,0.00005002676,0.00002161597,0.00001371006,0.00001271932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002874238,0.000003939947,0.0002690357,0.00001521939,0.000001968821,0.00001219023,0.000007889003,0.00004348985,0.9989347,0.00003863191,0.00003564903,0.0006084812],"study_design_scores_gemma":[0.000001922913,0.00005509602,0.006002879,0.000003808876,0.000007082661,0.00008727759,0.0000175597,0.000639855,0.991932,0.00004308141,0.001205887,0.000003521352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435792,0.001600296,0.04790984,0.0001913774,0.00007765845,0.00006742484,0.003772314,0.0004772725,0.00232458],"genre_scores_gemma":[0.9667985,0.001124753,0.02287519,0.000213355,0.00003328482,0.0001519404,0.002590445,0.0002091353,0.006003389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009953072,"threshold_uncertainty_score":0.003329635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01316093819955392,"score_gpt":0.2180539448901483,"score_spread":0.2048930066905943,"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."}}