{"id":"W2885815189","doi":"10.1021/acs.jproteome.8b00199","title":"Simultaneous Extraction of RNA and Metabolites from Single Kidney Tissue Specimens for Combined Transcriptomic and Metabolomic Profiling","year":2018,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"Robert Bosch Stiftung; Bosch-Forschungsstiftung","keywords":"Metabolomics; Transcriptome; RNA extraction; Metabolite profiling; Computational biology; RNA; Profiling (computer programming); Metabolome; Kidney; Biology; Chromatography; Chemistry; Biochemistry; Gene expression; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274183,0.0001301425,0.000394982,0.0002241017,0.0001331177,0.00004464501,0.0001425907,0.000108316,0.00001968075],"category_scores_gemma":[0.001386617,0.0001057028,0.00007224703,0.0001369011,0.0003023607,0.00001599622,0.00007486728,0.0001964053,6.501259e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001672953,"about_ca_system_score_gemma":0.0001074501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000222694,"about_ca_topic_score_gemma":0.000009223002,"domain_scores_codex":[0.9986717,0.0001260467,0.0004216555,0.0002393263,0.0002643079,0.0002770367],"domain_scores_gemma":[0.9984731,0.0001589157,0.00023722,0.0001535905,0.0008243158,0.0001528489],"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":[0.001105901,0.0001008853,0.0001584083,0.00006277826,0.0002477063,0.000002718665,0.00009585956,8.058011e-7,0.9961737,0.0001456414,0.000194589,0.001710949],"study_design_scores_gemma":[0.001365287,0.002452704,0.0004386381,0.00002878718,0.00007468022,0.00002365246,0.000180393,0.00009559012,0.9783397,0.001549395,0.01535175,0.0000994794],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878144,0.008034721,0.00285414,0.0003905608,0.0001585768,0.0005634973,0.00007803481,0.000002217296,0.0001038251],"genre_scores_gemma":[0.9716731,0.001955817,0.02549323,0.00001862088,0.0006038704,0.00001435374,0.000009784756,0.0000201854,0.0002110308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02263909,"threshold_uncertainty_score":0.4310437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085145072762633,"score_gpt":0.3575769318403709,"score_spread":0.3167254811127446,"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."}}