{"id":"W4311472042","doi":"10.1371/journal.pcbi.1010777","title":"Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Génome Québec; Compute Canada; Genome Canada","keywords":"Benchmark (surveying); Sequence (biology); Computer science; Set (abstract data type); Artificial intelligence; Algorithm; Data mining; Machine learning; Computational biology; Biology; 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":[0.0002740718,0.0004891325,0.0003302805,0.000291189,0.0002701616,0.0004299351,0.0004864035,0.0007040405,0.001028648],"category_scores_gemma":[0.001297733,0.0002516389,0.0005469832,0.0002520552,0.0003106967,0.0009192922,0.000306404,0.0007139875,0.0003124028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005162701,"about_ca_system_score_gemma":0.00051563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006806429,"about_ca_topic_score_gemma":0.007209583,"domain_scores_codex":[0.9998996,0.00001909638,0.000005493518,0.00003887403,0.0000187495,0.0000181915],"domain_scores_gemma":[0.9997098,0.0001341418,0.00005408671,0.00004221764,0.00003574385,0.00002401413],"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":[0.0001264417,0.00005947987,0.005651771,0.00006548573,0.00003595834,0.0001030993,0.00004038644,0.9591455,0.02279289,0.002663222,0.0007858726,0.008529936],"study_design_scores_gemma":[0.00000266201,0.00001256711,0.0008422804,0.000001710909,0.000002924697,0.00001545341,0.000005474048,0.9964716,0.001610647,0.0008112048,0.0002193989,0.000003989373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8602023,0.000416314,0.1328588,0.0002834851,0.00003756164,0.00003723026,0.001055867,0.0008911357,0.004217377],"genre_scores_gemma":[0.9852586,0.0001274659,0.01249566,0.0000419034,0.000009363395,0.00003273652,0.0009223787,0.00007280472,0.001039014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006806429,"threshold_uncertainty_score":0.01353365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436690464051722,"score_gpt":0.263765545727896,"score_spread":0.2493986410873788,"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."}}