{"id":"W2110103300","doi":"10.1101/gad.1788009","title":"Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes","year":2009,"lang":"en","type":"review","venue":"Genes & Development","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Genomics; National Cancer Institute; Ontario Genomics Institute; Genome Canada","keywords":"Biology; Computational biology; Profiling (computer programming); Transcriptome; Gene expression profiling; RNA; RNA-Seq; Microarray; Gene; Genetics; Gene expression; Computer science","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":[0.001554733,0.001031781,0.001468421,0.001424778,0.0003029876,0.001244153,0.001462396,0.001617787,0.0025673],"category_scores_gemma":[0.001103215,0.0003804439,0.0003987786,0.001809481,0.0009415658,0.00241604,0.0006719377,0.001750173,0.004293747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008706602,"about_ca_system_score_gemma":0.0008255317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005887443,"about_ca_topic_score_gemma":0.0009899121,"domain_scores_codex":[0.9995576,0.00007626396,0.00002966018,0.0000857624,0.0002222985,0.00002832754],"domain_scores_gemma":[0.9993094,0.000317173,0.0000772775,0.00003791984,0.0001693297,0.00008895197],"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":[0.00008644127,0.00004405818,0.0002272187,0.005816269,0.0000683735,0.0002373351,0.00008323796,0.001013436,0.01889892,0.009759214,0.02348355,0.940282],"study_design_scores_gemma":[0.00002180485,0.0001077615,0.0008845295,0.0008412659,0.00005901161,0.001370666,0.00009102449,0.0005222979,0.006049252,0.008359407,0.9816608,0.00003209666],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005520363,0.987678,0.006986167,0.0009024413,0.0006038719,0.00001725935,0.00004289783,0.0000730101,0.003144294],"genre_scores_gemma":[0.001735248,0.9906034,0.004517635,0.0006047801,0.0004345307,0.00002172777,0.00007750665,0.00001014498,0.001995031],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0025673,"threshold_uncertainty_score":0.008588433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05884307352828849,"score_gpt":0.3321873706509608,"score_spread":0.2733442971226723,"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."}}