{"id":"W2131525103","doi":"10.1039/c3mb70599d","title":"High-content screening of yeast mutant libraries by shotgun lipidomics","year":2014,"lang":"en","type":"article","venue":"Molecular BioSystems","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Danmarks Frie Forskningsfond; Lundbeckfonden; Natur og Univers, Det Frie Forskningsråd","keywords":"Lipidomics; Yeast; Shotgun; Mutant; Orbitrap; Saccharomyces cerevisiae; Metabolomics; Computational biology; High-throughput screening; Biology; Genetic screen; Lipid metabolism; Biochemistry; Chemistry; Mass spectrometry; Gene; Bioinformatics; Chromatography","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":[0.0007712598,0.001029597,0.0008023795,0.0009808083,0.0002719863,0.0007338176,0.0005356524,0.0003607026,0.000702414],"category_scores_gemma":[0.0005257487,0.0002704191,0.0005121132,0.0008289393,0.0002654729,0.000353161,0.0007562573,0.0005844961,0.0005187945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003427441,"about_ca_system_score_gemma":0.000345497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004652776,"about_ca_topic_score_gemma":0.0008124417,"domain_scores_codex":[0.9994816,0.00008197818,0.00006626171,0.0001000523,0.000213928,0.00005621284],"domain_scores_gemma":[0.9995847,0.0001059119,0.00007623986,0.0000768727,0.00009048649,0.00006575278],"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":[0.00002627631,0.00001860831,0.0001445128,0.0000141284,0.000007208132,0.00001720031,0.000004899125,0.00008196548,0.9988142,0.00002879126,0.00001078423,0.0008313502],"study_design_scores_gemma":[0.000007713705,0.0001503455,0.001716062,0.000002572148,0.00002623619,0.00010423,0.00001473599,0.00161229,0.9958397,0.00005800736,0.0004619549,0.000006159481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8589118,0.000444331,0.1340141,0.0001480487,0.00003429945,0.0004064124,0.003554182,0.001626975,0.0008597439],"genre_scores_gemma":[0.8474903,0.001165082,0.1409662,0.0001485308,0.00001767314,0.0005479013,0.006960751,0.0004767032,0.00222692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001029597,"threshold_uncertainty_score":0.004078805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009989268225396057,"score_gpt":0.1977230524215808,"score_spread":0.1877337841961847,"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."}}