{"id":"W2767108658","doi":"10.1016/j.yrtph.2017.11.001","title":"A generic Transcriptomics Reporting Framework (TRF) for ‘omics data processing and analysis","year":2017,"lang":"en","type":"review","venue":"Regulatory Toxicology and Pharmacology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Omics; Raw data; Computer science; Outlier; Profiling (computer programming); Data mining; Data processing; Context (archaeology); Computational biology; Bioinformatics; Biology; Artificial intelligence; Database","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02329215,0.002935867,0.002414621,0.01285771,0.00110075,0.006232065,0.006003829,0.003913487,0.01300547],"category_scores_gemma":[0.01969535,0.001152475,0.002729519,0.01173445,0.002355328,0.006513825,0.004318045,0.004843626,0.02188403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003265669,"about_ca_system_score_gemma":0.008147185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004045702,"about_ca_topic_score_gemma":0.002307955,"domain_scores_codex":[0.9904678,0.002782132,0.002159889,0.000742016,0.003452953,0.0003951995],"domain_scores_gemma":[0.9852759,0.005694752,0.001727849,0.002011205,0.004827481,0.0004628074],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007954707,0.0001079109,0.0004862877,0.01252631,0.0001290357,0.0006526291,0.0006551359,0.00247173,0.009469626,0.1845521,0.2135081,0.5753616],"study_design_scores_gemma":[0.000007030563,0.00002775303,0.0002169126,0.001591845,0.0000291847,0.000399175,0.00008188941,0.000358267,0.002033833,0.009878653,0.9853307,0.00004477803],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001183243,0.1277274,0.758743,0.012729,0.005185346,0.002792306,0.01694621,0.01559116,0.05910238],"genre_scores_gemma":[0.00740236,0.142559,0.7689673,0.009149399,0.002634795,0.004851921,0.03778681,0.002584329,0.02406394],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9767079,"threshold_uncertainty_score":0.1231822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2042202822474676,"score_gpt":0.4552941525103952,"score_spread":0.2510738702629276,"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."}}