{"id":"W4229003553","doi":"10.1111/febs.15832","title":"Computational modelling of stem cell–niche interactions facilitates discovery of strategies to enhance tissue regeneration and counteract ageing","year":2021,"lang":"en","type":"review","venue":"FEBS Journal","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Horizon 2020 Framework Programme; Ministerio de Economía y Competitividad; Centro Nacional de Investigaciones Cardiovasculares; European Molecular Biology Organization; Fonds National de la Recherche Luxembourg; H2020 European Research Council; “la Caixa” Foundation; Muscular Dystrophy Association","keywords":"Stem cell; Niche; Biology; Cell biology; Stem cell niche; Regeneration (biology); Progenitor cell; Interactome; Gene; Genetics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001252417,0.0001612801,0.0004713411,0.00006831178,0.00007433477,0.00005296709,0.00009805777,0.000106278,0.00001094165],"category_scores_gemma":[0.00001078714,0.0001434407,0.0001459064,0.0000635351,0.00004499059,0.00002413239,0.00005621445,0.0001920154,8.533677e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001185024,"about_ca_system_score_gemma":0.0002782477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007102202,"about_ca_topic_score_gemma":0.000007979889,"domain_scores_codex":[0.9990205,0.0001484066,0.0004262555,0.0002090177,0.00009211596,0.0001036969],"domain_scores_gemma":[0.9992515,0.00006668559,0.0003745879,0.0001179153,0.0001504307,0.00003891077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001347153,0.0002337999,0.000008129987,0.008173871,0.0007462285,0.00001630325,0.0007799806,0.4412587,0.05411813,0.0002425406,0.00144546,0.4928422],"study_design_scores_gemma":[0.000872666,0.001882224,0.00003852024,0.02081103,0.001255392,0.000580902,0.004488191,0.004684933,0.05703693,0.001216067,0.9052887,0.001844466],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0243182,0.8557677,0.1194812,0.00001305007,0.0001543269,0.0001096777,0.00007577361,0.000001335123,0.00007874516],"genre_scores_gemma":[0.1110696,0.8870826,0.001205123,0.00001290538,0.000112972,0.000007991967,0.0002608948,0.00001338908,0.0002345447],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9038432,"threshold_uncertainty_score":0.5849342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03507883210823701,"score_gpt":0.3413568205971256,"score_spread":0.3062779884888886,"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."}}