{"id":"W2604655536","doi":"10.1172/jci.insight.91634","title":"LipidFinder: A computational workflow for discovery of lipids identifies eicosanoid-phosphoinositides in platelets","year":2017,"lang":"en","type":"article","venue":"JCI Insight","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"British Heart Foundation; Wellcome Trust","keywords":"Workflow; Computer science; Python (programming language); Analytics; Health informatics tools; Lipidomics; Platelet; Metabolomics; Eicosanoid; Biomarker discovery; Bioinformatics; Informatics; Computational biology; Data science; Chemistry; Arachidonic acid; Database; Biology; Biochemistry; Proteomics; Programming language; Immunology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000136636,0.0001403145,0.0002630223,0.00007394028,0.0001746583,0.00009045718,0.0002485337,0.0001000368,0.000006184318],"category_scores_gemma":[0.0001994385,0.000128118,0.000123689,0.00004250082,0.0001324862,0.00001826551,0.0001775713,0.00005726008,0.000001712945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001092083,"about_ca_system_score_gemma":0.00005396631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003715028,"about_ca_topic_score_gemma":0.000258992,"domain_scores_codex":[0.999123,0.0000169396,0.0002674095,0.0002856499,0.000109558,0.0001974707],"domain_scores_gemma":[0.9993106,0.00003956271,0.0001960983,0.0003457207,0.0000795199,0.00002851014],"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":[0.0006195807,0.0004578581,0.04846657,0.0001903345,0.0004223655,0.000009725003,0.0003488393,0.0005463333,0.9209967,0.01604102,0.008634309,0.003266307],"study_design_scores_gemma":[0.002489509,0.0004104127,0.1769007,0.00008224054,0.0000429309,0.000006369455,0.00009236694,0.0002626616,0.7723218,0.008412078,0.03853065,0.0004482505],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931942,0.001483125,0.00270599,0.0002528833,0.0004972122,0.0002268475,0.00006384857,0.000004458013,0.001571425],"genre_scores_gemma":[0.9963089,0.000425623,0.002027803,0.00008760476,0.0002402949,0.00004118932,0.0000825794,0.00001550841,0.0007704748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1486749,"threshold_uncertainty_score":0.52245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644325892720787,"score_gpt":0.2775895262223391,"score_spread":0.2611462672951312,"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."}}