{"id":"W4391445811","doi":"10.1101/2024.01.30.577990","title":"Endogenous labeling empowers accurate detection of m <sup>6</sup> A from single long reads of direct RNA sequencing","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Science and Technology Planning Project of Guangdong Province; Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"RNA; Endogeny; Computational biology; Genetics; Biology; Computer science; Gene; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005186806,0.0004348955,0.000288508,0.0003009528,0.0001929449,0.0004566366,0.0004124343,0.0005514456,0.001493735],"category_scores_gemma":[0.001277353,0.0002856571,0.00032696,0.0002469532,0.000342432,0.0003946364,0.0005461007,0.0008933988,0.0007821036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002773289,"about_ca_system_score_gemma":0.0002690158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008224087,"about_ca_topic_score_gemma":0.002872138,"domain_scores_codex":[0.9996768,0.00003862346,0.00001555585,0.0001516238,0.0000885487,0.00002874769],"domain_scores_gemma":[0.9994173,0.000202022,0.0001514737,0.00008397848,0.0001106841,0.00003466412],"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":[0.0003095857,0.0000551251,0.007329767,0.0002550605,0.00003789032,0.00007853918,0.00007898935,0.01081229,0.938421,0.001091712,0.000731513,0.04079857],"study_design_scores_gemma":[0.0000167491,0.0002659422,0.01694629,0.00004037667,0.00003691684,0.0001888909,0.00008270743,0.1989092,0.7749111,0.002336479,0.006218316,0.00004701104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7215311,0.00122582,0.2646423,0.0002399861,0.0001033309,0.00006518961,0.00369761,0.00325609,0.005238525],"genre_scores_gemma":[0.8153014,0.0004742809,0.1754963,0.0002855743,0.00003041687,0.0001127494,0.004021394,0.0003725294,0.003905339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001493735,"threshold_uncertainty_score":0.004997015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360125985149157,"score_gpt":0.2303205961577611,"score_spread":0.2067193363062695,"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."}}