{"id":"W4388831919","doi":"10.1002/imt2.148","title":"VeloPro: A pipeline integrating Ribo‐seq and AlphaFold deciphers association patterns between translation velocity and protein structure features","year":2023,"lang":"en","type":"article","venue":"iMeta","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Japan Association for Chemical Innovation; Cybermedia Center, Osaka University; Japan Society for the Promotion of Science; Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"Pipeline (software); Translation (biology); Association (psychology); Artificial intelligence; Computer science; Computational biology; Natural language processing; Biology; Psychology; Genetics; Gene; Messenger RNA","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002342183,0.003527844,0.002127931,0.002374009,0.001343256,0.003097386,0.001829555,0.0009438238,0.01065601],"category_scores_gemma":[0.003577606,0.001785737,0.002514577,0.001301298,0.0006239013,0.001290086,0.001748122,0.002423387,0.008728309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008421912,"about_ca_system_score_gemma":0.001778946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002726265,"about_ca_topic_score_gemma":0.005484911,"domain_scores_codex":[0.9989975,0.0001152962,0.0000834563,0.0005229312,0.0001692528,0.0001115379],"domain_scores_gemma":[0.9987944,0.0005461259,0.0001622292,0.0002072113,0.0001838234,0.0001061353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003898598,0.0003281213,0.03280341,0.006767293,0.002120351,0.0008893908,0.001049145,0.02314648,0.5594208,0.008524121,0.1684189,0.1926335],"study_design_scores_gemma":[0.0006181778,0.0006881378,0.04756058,0.0004028018,0.0007087896,0.001211031,0.0006224184,0.4242138,0.3332687,0.01889103,0.1710498,0.0007647001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05740885,0.00202271,0.5716044,0.000323196,0.0005349206,0.0005115893,0.1116819,0.2501318,0.005780673],"genre_scores_gemma":[0.1597389,0.001181303,0.5691282,0.0008823596,0.0001954861,0.002361326,0.219787,0.03972241,0.007003123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01065601,"threshold_uncertainty_score":0.03564787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364879990830844,"score_gpt":0.2544328650060612,"score_spread":0.2407840650977527,"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."}}