{"id":"W4415396776","doi":"10.1101/2025.10.20.683427","title":"Bioinformatic analysis of differentially expressed long non-coding RNAs in skeletal muscle following aerobic and resistance exercise","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletal muscle; Resistance training; Aerobic exercise; Gene expression; Modalities; Endurance training; RNA; Physical exercise","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":[0.0004885047,0.0002554846,0.0004823915,0.001085859,0.0004742233,0.0007135118,0.000258034,0.0003175121,0.002047123],"category_scores_gemma":[0.0008178525,0.0001706538,0.0006645895,0.001100834,0.0002555682,0.0002958324,0.0004187166,0.0004787436,0.000829339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002842425,"about_ca_system_score_gemma":0.0004226326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001164834,"about_ca_topic_score_gemma":0.002649721,"domain_scores_codex":[0.9995764,0.00003275236,0.00003136179,0.0002032491,0.0001002636,0.00005608259],"domain_scores_gemma":[0.9995197,0.0001991839,0.0001186568,0.00003621712,0.0000872664,0.00003898096],"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":[0.001492095,0.0001488338,0.1105395,0.001952995,0.0004467606,0.0007985182,0.0006229865,0.003755121,0.8103123,0.000851981,0.005359548,0.06371945],"study_design_scores_gemma":[0.00008091288,0.0004560002,0.8049552,0.0001980607,0.0005337608,0.001383323,0.000944558,0.04294244,0.1205247,0.002319005,0.02552484,0.0001372162],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.915436,0.003628073,0.02498136,0.0005141821,0.0001421331,0.0001290077,0.04998115,0.001864624,0.003323513],"genre_scores_gemma":[0.8832166,0.001191528,0.04527884,0.0006295468,0.0000725371,0.0003259782,0.06538326,0.0005127951,0.003388777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002047123,"threshold_uncertainty_score":0.006848276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006872003111067206,"score_gpt":0.2307804975751853,"score_spread":0.2239084944641181,"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."}}