{"id":"W2943714341","doi":"10.3390/metabo9050101","title":"A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium","year":2019,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Toronto Centre for Phenogenomics; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"National Cancer Institute; National Institutes of Health; National Institute of Environmental Health Sciences; Genome Canada; Ontario Genomics; National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Metabolomics; Gene knockout; Computational biology; Phenotype; KEGG; Biology; Chemistry; Gene; Bioinformatics; Biochemistry; Transcriptome; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003869754,0.0002354531,0.0004589415,0.00008128477,0.00008748348,0.0000414563,0.000516858,0.00009102601,0.00004884617],"category_scores_gemma":[0.0002813488,0.0001719564,0.0001707538,0.0001028446,0.00014403,0.00001036882,0.0003171028,0.0001004951,0.00001176488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008635399,"about_ca_system_score_gemma":0.00006453697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003356658,"about_ca_topic_score_gemma":0.00001999827,"domain_scores_codex":[0.9985919,0.00006265624,0.0004411848,0.0004395229,0.000201026,0.0002636946],"domain_scores_gemma":[0.9986495,0.0001077306,0.0003194184,0.0006041555,0.0002677959,0.00005142401],"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":[0.0002538566,0.00006919161,0.008273187,0.00003609899,0.0009567621,1.796948e-7,0.00005793345,0.0001569245,0.9782608,0.006974096,0.004818752,0.0001421801],"study_design_scores_gemma":[0.0009010626,0.00005836373,0.0008600586,0.000003148749,0.0001334089,0.000003710853,0.0002736414,0.0005729141,0.6866225,0.0001120183,0.3102788,0.0001803333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887285,0.003127319,0.0005530715,0.0001851966,0.0005870555,0.0007401395,0.005763059,0.00001035455,0.0003052994],"genre_scores_gemma":[0.9835482,0.001225049,0.007517376,0.0008267806,0.0002892126,0.0001500848,0.003356424,0.00005108847,0.003035735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3054601,"threshold_uncertainty_score":0.7012177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152192357700459,"score_gpt":0.2594901249709912,"score_spread":0.2442708892009453,"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."}}