{"id":"W3185042382","doi":"10.1093/bioinformatics/btab527","title":"miRAnno—network-based functional microRNA annotation","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Discovery Centre; University Health Network","funders":"Canada Foundation for Innovation; University Health Network; International Business Machines Corporation","keywords":"Annotation; microRNA; Computer science; Computational biology; Noise (video); Data mining; Artificial intelligence; Biology; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001006285,0.0001115246,0.00008088477,0.0000252762,0.00008580125,0.00003889247,0.0000663812,0.0001142664,0.00009741647],"category_scores_gemma":[0.0000606517,0.0001179901,0.00008851963,0.000132201,0.00003567612,0.000007844099,0.00004101015,0.00004550147,0.00007907819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002102904,"about_ca_system_score_gemma":0.0002294427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.724775e-7,"about_ca_topic_score_gemma":0.000005718175,"domain_scores_codex":[0.9992625,0.00002641321,0.0002580984,0.0001276693,0.0001437961,0.0001815511],"domain_scores_gemma":[0.9993408,0.000009306003,0.0001075618,0.0002706031,0.0002059243,0.00006585193],"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":[0.000161374,0.0001479231,0.003641987,0.0002159713,0.0001318265,0.000007651491,0.00007113086,0.02179747,0.8269628,0.0004469135,0.1378449,0.00857008],"study_design_scores_gemma":[0.002320546,0.0001378228,0.05955137,0.00007496212,0.00008486963,0.00006839736,0.0001234769,0.03818753,0.578842,0.0002389216,0.3196749,0.0006952337],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7653007,0.001548939,0.2257631,0.0005484726,0.0008822386,0.0003521878,0.0001200518,0.00007663241,0.005407756],"genre_scores_gemma":[0.9294459,0.00003945534,0.06091531,0.002126864,0.0007480274,0.00002396292,0.005643597,0.00003455464,0.001022346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2481208,"threshold_uncertainty_score":0.4811496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222106977605556,"score_gpt":0.2265008037804691,"score_spread":0.2142797340044135,"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."}}