{"id":"W2738019036","doi":"10.1038/nature22293","title":"Can we predict protein from mRNA levels?","year":2017,"lang":"en","type":"article","venue":"Nature","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":240,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Messenger RNA; Computational biology; Chemistry; Biology; Genetics; Gene","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":[0.001411562,0.0009409622,0.001363446,0.001133774,0.0002570378,0.001912183,0.0009262886,0.001866939,0.001676933],"category_scores_gemma":[0.006692141,0.0007022845,0.0006572526,0.0009884047,0.001429382,0.004800128,0.000481957,0.002330774,0.002993964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006406567,"about_ca_system_score_gemma":0.0004470239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009250657,"about_ca_topic_score_gemma":0.0008244201,"domain_scores_codex":[0.9995832,0.00008584987,0.00002927423,0.0001438038,0.0001230024,0.00003483992],"domain_scores_gemma":[0.9969196,0.001876803,0.0002925432,0.0004278382,0.000348928,0.0001342395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001428954,0.0003160113,0.06123292,0.00131908,0.0006597899,0.000521336,0.0001530602,0.04071113,0.4813701,0.05099037,0.02169041,0.3396069],"study_design_scores_gemma":[0.00009052313,0.000444859,0.03456017,0.0001849386,0.0003669993,0.001181354,0.0001509674,0.2266188,0.2732194,0.4112289,0.05172182,0.0002311334],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2568942,0.05128554,0.6408486,0.02269765,0.004280174,0.00005418222,0.005047049,0.005255323,0.01363718],"genre_scores_gemma":[0.7985298,0.03031957,0.1423711,0.009295771,0.002562818,0.0001330307,0.004008866,0.001331896,0.01144714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001912183,"threshold_uncertainty_score":0.007465124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187762727751305,"score_gpt":0.2518960804738288,"score_spread":0.2400184531963157,"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."}}