{"id":"W2461654689","doi":"10.1080/19485565.2016.1185600","title":"Genome-Wide Profiling of RNA from Dried Blood Spots: Convergence with Bioinformatic Results Derived from Whole Venous Blood and Peripheral Blood Mononuclear Cells","year":2016,"lang":"en","type":"article","venue":"Biodemography and Social Biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Aging; National Institutes of Health","keywords":"Venipuncture; Peripheral blood mononuclear cell; Transcriptome; Biology; Gene expression profiling; Gene expression; Population; Dried blood spot; Venous blood; Immunology; Gene; Computational biology; Bioinformatics; Medicine; Genetics; Endocrinology; In vitro","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":[0.001171493,0.0002674338,0.0003535402,0.001047533,0.0003412833,0.0008452854,0.0002032445,0.0002941537,0.0006308002],"category_scores_gemma":[0.002147843,0.0001503902,0.0002598,0.0008649703,0.0004650065,0.0002446616,0.0003608382,0.0003034191,0.0003257736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001703933,"about_ca_system_score_gemma":0.0002054347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000640603,"about_ca_topic_score_gemma":0.001436628,"domain_scores_codex":[0.9990054,0.0002927605,0.00004149959,0.0002860556,0.0003260163,0.00004834358],"domain_scores_gemma":[0.9991482,0.0004366626,0.0001332621,0.00008342894,0.0001645611,0.00003376248],"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":[0.0003264969,0.00006931462,0.06513066,0.000201792,0.0001980141,0.0001345143,0.0005555468,0.0007828881,0.9027935,0.0004928252,0.0003109185,0.02900356],"study_design_scores_gemma":[0.00002118449,0.0003623475,0.8191552,0.00005027337,0.0002212229,0.001077799,0.0008632052,0.00739269,0.1629389,0.002204868,0.005667945,0.00004431418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.932443,0.001323765,0.06001702,0.0002387798,0.0000487759,0.000089443,0.002654301,0.0001777652,0.00300714],"genre_scores_gemma":[0.9357823,0.0009134781,0.05731437,0.000370276,0.0001008517,0.000172455,0.004115153,0.00009265529,0.001138495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001171493,"threshold_uncertainty_score":0.006195545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00745421163855044,"score_gpt":0.1862219329869899,"score_spread":0.1787677213484394,"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."}}