{"id":"W4379230391","doi":"10.3390/biology12060812","title":"RNA Sequencing of Pooled Samples Effectively Identifies Differentially Expressed Genes","year":2023,"lang":"en","type":"article","venue":"Biology","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Van Andel Research Institute","keywords":"Biology; RNA; Gene; Genetics; RNA-Seq; Deep sequencing; Mutant; DNA sequencing; Computational biology; Genome; Gene expression; Transcriptome","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002059533,0.0001773078,0.0002569831,0.00009397414,0.00007708102,0.00001468706,0.0002789669,0.0002145811,0.00003585953],"category_scores_gemma":[0.0001416027,0.0001641751,0.000117855,0.00009929753,0.0001778805,0.000002509147,0.0002223305,0.00006117237,0.00003385905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001107811,"about_ca_system_score_gemma":0.00006644396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001162963,"about_ca_topic_score_gemma":0.00006984524,"domain_scores_codex":[0.9988127,0.0001506554,0.0002552053,0.0003952103,0.00007869469,0.0003075639],"domain_scores_gemma":[0.9993257,0.00004686544,0.0001203653,0.0003547668,0.00009852819,0.00005379634],"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":[0.00003063834,0.00001828973,0.006365965,0.00004456204,0.0001072706,0.000001729215,0.0001921563,0.00008296752,0.9911337,0.0001255047,0.0004374002,0.001459845],"study_design_scores_gemma":[0.0003618734,0.0002134082,0.02481168,0.00001073103,0.00003814476,0.000003278901,0.0001146746,0.00007231611,0.9714701,0.001193238,0.00152365,0.0001869577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903527,0.0007243989,0.007988014,0.00005849852,0.0004580044,0.0001644877,0.00006581534,0.00004637703,0.0001417095],"genre_scores_gemma":[0.997969,0.0005539015,0.0004847226,0.00006302721,0.0002372124,0.00002225699,0.000268542,0.0000266163,0.0003746755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01966363,"threshold_uncertainty_score":0.6694867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02552522549216982,"score_gpt":0.2628887424395615,"score_spread":0.2373635169473916,"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."}}