{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003527226,0.0001418658,0.0001540949,0.00004152588,0.0001021555,0.00005554926,0.00007396527,0.0002278422,0.00001065102],"category_scores_gemma":[0.000175491,0.0001250919,0.00003714999,0.00009875366,0.00001571909,0.000008601159,0.00004356383,0.0001028075,0.000001723918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001238125,"about_ca_system_score_gemma":0.00002073981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005523155,"about_ca_topic_score_gemma":0.0001190468,"domain_scores_codex":[0.9991305,0.00009605526,0.0001604487,0.0002866311,0.0001420201,0.0001842983],"domain_scores_gemma":[0.9996388,0.00003530659,0.0001014782,0.0001259281,0.00004330164,0.0000552089],"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":[0.0000226198,0.000003849041,0.01067368,0.00003146904,0.00006690659,0.00000113888,0.0001561612,0.000002867568,0.8847471,0.00002575489,0.0003170015,0.1039514],"study_design_scores_gemma":[0.0004987739,0.0001056504,0.05338739,0.00004856047,0.00006172284,0.000002929179,0.0001096641,0.0001615003,0.9408581,0.001249216,0.003263686,0.0002527856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995153,0.0004266334,0.003252193,0.0005612274,0.00004861869,0.000336593,0.0001070075,0.00002969135,0.00008501706],"genre_scores_gemma":[0.9954355,0.00007074457,0.003392088,0.00005699332,0.0001591576,0.00002258196,0.0002250033,0.00001964579,0.0006183003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1036986,"threshold_uncertainty_score":0.5101098,"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."}}