{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009606758,0.0009638008,0.000660698,0.001345916,0.0006378778,0.0005406701,0.0004430351,0.0006011532,0.001282716],"category_scores_gemma":[0.0007028774,0.0003809495,0.0006861819,0.0008653195,0.000521511,0.0004284685,0.0006312176,0.0009550319,0.001286622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001669537,"about_ca_system_score_gemma":0.0008293565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002570261,"about_ca_topic_score_gemma":0.001302529,"domain_scores_codex":[0.999252,0.0001100676,0.00008463363,0.0002610582,0.0002132634,0.00007907802],"domain_scores_gemma":[0.9995265,0.0001316263,0.00008074034,0.0001278567,0.0001015479,0.00003168366],"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":[0.00003332798,0.00001344678,0.0001754064,0.00004638177,0.000008467267,0.00003340999,0.000009431983,0.00003043489,0.9979036,0.0000325161,0.00002384239,0.001689854],"study_design_scores_gemma":[0.00001712637,0.00033778,0.008840221,0.00002553434,0.00008994013,0.0003997579,0.00004837269,0.001055753,0.983891,0.0002910364,0.004985429,0.00001810481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3610598,0.005919681,0.6101666,0.000367076,0.0004438592,0.003039027,0.01335,0.001390842,0.004263152],"genre_scores_gemma":[0.3001971,0.00632972,0.6645942,0.0003473107,0.0002461006,0.004020601,0.01868181,0.0004423062,0.005140819],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001345916,"threshold_uncertainty_score":0.00508064,"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."}}