{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001305581,0.001287129,0.001305505,0.002514953,0.0005977178,0.001253995,0.001306028,0.001441895,0.006785226],"category_scores_gemma":[0.002325843,0.0003386721,0.001262996,0.003338319,0.000250519,0.000489521,0.001614662,0.001149322,0.004482965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007794508,"about_ca_system_score_gemma":0.001607758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006698071,"about_ca_topic_score_gemma":0.008346163,"domain_scores_codex":[0.9990193,0.0001330423,0.0001331412,0.0003624781,0.0002416906,0.000110398],"domain_scores_gemma":[0.9983163,0.0003116929,0.000368389,0.0003860617,0.0003556933,0.0002619169],"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":[0.005598876,0.001159306,0.1741257,0.004718359,0.002644531,0.002232441,0.0002633351,0.008210738,0.06314374,0.003041991,0.6341465,0.1007146],"study_design_scores_gemma":[0.000878159,0.0006623359,0.3735197,0.0007317682,0.0009308021,0.002582794,0.0002454894,0.006123188,0.0159128,0.003933585,0.5941908,0.0002886455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01416315,0.0002797389,0.001686396,0.0001336526,0.00002222834,0.00007909052,0.9826319,0.0003816288,0.0006221725],"genre_scores_gemma":[0.008106299,0.0001397495,0.002592103,0.00007184695,0.00001098158,0.0002267511,0.9884903,0.00005008478,0.0003119074],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.006785226,"threshold_uncertainty_score":0.02269882,"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."}}