{"id":"W2802703971","doi":"10.1016/j.bbrc.2018.04.075","title":"Sphingolipidomics analysis of large clinical cohorts. Part 2: Potential impact and applications","year":2018,"lang":"en","type":"review","venue":"Biochemical and Biophysical Research Communications","topic":"Sphingolipid Metabolism and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National University Health System; Ministry of Education - Singapore","keywords":"Computational biology; Function (biology); Biology; Sphingolipid; Identification (biology); Pathological; Lipidomics; Metabolic pathway; Bioinformatics; Medicine; Cell biology; Biochemistry; Metabolism; Pathology; Ecology","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":[0.004419117,0.001387234,0.00248555,0.002257086,0.000179194,0.001827543,0.001373323,0.001678258,0.001688246],"category_scores_gemma":[0.004596756,0.000420104,0.001172966,0.0020927,0.0008616126,0.001256714,0.001104193,0.00221294,0.00095672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009013574,"about_ca_system_score_gemma":0.001903992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001298993,"about_ca_topic_score_gemma":0.002072864,"domain_scores_codex":[0.9993283,0.0001900554,0.00007799002,0.0001843978,0.0001693972,0.00004992741],"domain_scores_gemma":[0.9965335,0.002292413,0.0002756223,0.000104918,0.0006382769,0.0001552792],"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":[0.0002753242,0.00005096998,0.002656607,0.009594095,0.000627251,0.0002909764,0.00005487511,0.0002703473,0.002520751,0.003221157,0.02547728,0.9549604],"study_design_scores_gemma":[0.0002092431,0.0003694406,0.02381315,0.016703,0.002216919,0.006067073,0.0002040263,0.0009603853,0.00410295,0.01801642,0.9271656,0.0001718087],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000213419,0.9979966,0.0004177481,0.000791812,0.0002120014,0.00000755148,0.0000977863,0.000009466939,0.0002536314],"genre_scores_gemma":[0.002043968,0.9950882,0.0009076322,0.0008063299,0.0007129133,0.00001987318,0.0001833619,0.000005729913,0.0002319026],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004419117,"threshold_uncertainty_score":0.0233708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165448733239162,"score_gpt":0.4872950499138527,"score_spread":0.3707501765899365,"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."}}