{"id":"W2098350013","doi":"10.1101/gr.3274805","title":"Analysis of long-lived <i>C. elegans daf-2</i> mutants using serial analysis of gene expression","year":2005,"lang":"en","type":"article","venue":"Genome Research","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":207,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Institute on Aging; National Institutes of Health; Michael Smith Health Research BC; Canada's Michael Smith Genome Sciences Centre; Canadian Institutes of Health Research; Austrian Science Fund","keywords":"Biology; Caenorhabditis elegans; Gene; Mutant; Genetics; Gene expression; Longevity; Serial analysis of gene expression; Microarray analysis techniques; Caenorhabditis; Gene expression profiling; Regulation of gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009710957,0.0004951254,0.0002343297,0.0003940886,0.0002398508,0.0002192446,0.0002044344,0.0002135666,0.000596702],"category_scores_gemma":[0.000117013,0.0001412887,0.0002833688,0.000164193,0.0002251749,0.0001364502,0.000137738,0.0004452345,0.0002071516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003346871,"about_ca_system_score_gemma":0.000158364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004325,"about_ca_topic_score_gemma":0.002929711,"domain_scores_codex":[0.9999141,0.000004859414,0.000007537398,0.00003081495,0.00002752106,0.00001524289],"domain_scores_gemma":[0.9998272,0.00003559727,0.00005677068,0.00001502715,0.00002420405,0.00004131562],"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":[0.00001728741,0.000006175384,0.0001796229,0.00001223025,0.000002073145,0.0000193102,0.000005008256,0.00002498954,0.9993358,0.00002233571,0.00001238412,0.0003627793],"study_design_scores_gemma":[0.000009800477,0.0001704254,0.02265037,0.00000897942,0.00002469718,0.000272715,0.00002694824,0.0008733179,0.9744658,0.00006592105,0.001419226,0.00001178914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898667,0.0005044111,0.006832314,0.00005223378,0.00002033616,0.00003128292,0.001778241,0.0001826594,0.0007317995],"genre_scores_gemma":[0.972551,0.001050189,0.01844636,0.0001515988,0.00001647041,0.0001586602,0.003486211,0.0001831791,0.003956514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001004325,"threshold_uncertainty_score":0.002428293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05735388689013503,"score_gpt":0.353644868706439,"score_spread":0.2962909818163039,"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."}}