{"id":"W1966647233","doi":"10.1371/journal.pone.0013340","title":"Genome-Wide Data-Mining of Candidate Human Splice Translational Efficiency Polymorphisms (STEPs) and an Online Database","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Nutrition, Metabolism and Diabetes; Medical Research Council","keywords":"Biology; International HapMap Project; Genetics; Human genome; Intron; Alternative splicing; Genome; Candidate gene; Gene; RNA splicing; Computational biology; Coding region; Exon; 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":[0.001070481,0.0003703449,0.0006315365,0.003571182,0.0003301316,0.000752233,0.0007704326,0.0006659726,0.002336551],"category_scores_gemma":[0.004391728,0.000170142,0.0007469965,0.004968768,0.0001784752,0.0004040764,0.0006193435,0.0004608646,0.001101823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002954034,"about_ca_system_score_gemma":0.0009049282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001661985,"about_ca_topic_score_gemma":0.003564419,"domain_scores_codex":[0.9990059,0.0001425295,0.0001965993,0.0004317625,0.0001615594,0.00006168433],"domain_scores_gemma":[0.9970713,0.001300115,0.0006874809,0.0003489237,0.0003649424,0.0002272484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004384506,0.0007760414,0.7269945,0.004720246,0.001725351,0.004067184,0.0006771617,0.007271275,0.06070329,0.002782806,0.03245926,0.1534383],"study_design_scores_gemma":[0.0003482161,0.0008877851,0.8575501,0.000532746,0.001342299,0.005522931,0.0008606333,0.03337912,0.03667677,0.004176743,0.05859156,0.0001311498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6297859,0.002051134,0.01147247,0.0003082702,0.00003768456,0.0002412013,0.3525347,0.001596094,0.0019726],"genre_scores_gemma":[0.4771444,0.0007597979,0.03583134,0.0001734972,0.00002700388,0.0004306302,0.484703,0.0001349504,0.0007953689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003571182,"threshold_uncertainty_score":0.007816553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06103936725145525,"score_gpt":0.302701258009318,"score_spread":0.2416618907578627,"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."}}