{"id":"W2097902665","doi":"10.7150/jgen.3781","title":"Adipogenic Transcriptome Profiling Using High Throughput Technologies","year":2013,"lang":"en","type":"review","venue":"Journal of Genomics","topic":"Adipose Tissue and Metabolism","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Adipogenesis; Computational biology; Profiling (computer programming); Biology; Transcriptome; Gene expression profiling; DNA microarray; microRNA; Genomics; RNA interference; Functional genomics; Gene; Gene expression; Genome; Genetics; RNA; Computer science","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":[0.001098391,0.0008476893,0.001815984,0.002050915,0.0002204617,0.001214486,0.0007862617,0.0006079595,0.001369236],"category_scores_gemma":[0.0005092565,0.000380568,0.0006747774,0.002066008,0.00035515,0.0007935737,0.0006661628,0.001269216,0.002362436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003840432,"about_ca_system_score_gemma":0.0004477851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003353514,"about_ca_topic_score_gemma":0.0005326386,"domain_scores_codex":[0.9994028,0.0001027629,0.00003829171,0.0001237967,0.0002895328,0.00004290775],"domain_scores_gemma":[0.999633,0.0001487368,0.00005206016,0.00002722854,0.0001194676,0.00001942825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001637048,0.00008016848,0.0009969012,0.009076525,0.0001835245,0.0004712658,0.0001121933,0.001133083,0.417334,0.003538443,0.0103957,0.5565144],"study_design_scores_gemma":[0.00003039284,0.0002663236,0.01325473,0.001577355,0.0004383206,0.003535286,0.0001725235,0.002171285,0.288293,0.004309143,0.6858016,0.0001500715],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.008638097,0.921914,0.05643431,0.0008696954,0.0005927104,0.0001326738,0.001686176,0.0005399303,0.009192418],"genre_scores_gemma":[0.01939906,0.9433934,0.02825596,0.0006419569,0.0004112732,0.0002103372,0.002614863,0.00008013938,0.004993047],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002050915,"threshold_uncertainty_score":0.005808949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09861866436370076,"score_gpt":0.3575135159767122,"score_spread":0.2588948516130114,"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."}}