{"id":"W2984769480","doi":"10.1093/nar/gkz933","title":"Xenbase: deep integration of GEO & SRA RNA-seq and ChIP-seq data in a model organism database","year":2019,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Wellcome Trust; Wellcome","keywords":"Biology; RNA-Seq; Organism; Database; RNA; Computational biology; Model organism; Genetics; Gene; Gene expression; Transcriptome; 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":[0.002999481,0.002492331,0.002499829,0.006316582,0.001490843,0.004426321,0.00460047,0.001492672,0.01509818],"category_scores_gemma":[0.005015907,0.001678912,0.001450518,0.006581237,0.0006366428,0.002538659,0.00539835,0.002127543,0.01780697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001409434,"about_ca_system_score_gemma":0.003387875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006519873,"about_ca_topic_score_gemma":0.00919071,"domain_scores_codex":[0.9984018,0.0001506324,0.0001961956,0.0005812409,0.0005382756,0.0001317913],"domain_scores_gemma":[0.9980506,0.0003366074,0.0003038486,0.0007042512,0.0003558657,0.0002489012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002172716,0.0003486473,0.01359408,0.006326353,0.001444295,0.001238988,0.001094838,0.01125623,0.1198562,0.01640093,0.7365704,0.08969639],"study_design_scores_gemma":[0.0004514735,0.0002688916,0.02314091,0.0007791875,0.0006915775,0.001022951,0.0004331016,0.02817227,0.06948498,0.01541438,0.8597901,0.0003501731],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01169053,0.001356372,0.08390351,0.0003597759,0.0003529239,0.0002584372,0.7561054,0.1381372,0.00783589],"genre_scores_gemma":[0.01501257,0.0008305941,0.05954155,0.0002485218,0.00003640619,0.0004773612,0.9126005,0.009551669,0.001700856],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01509818,"threshold_uncertainty_score":0.05050844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0716304473230731,"score_gpt":0.349149408186832,"score_spread":0.2775189608637589,"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."}}