{"id":"W2762486785","doi":"10.1021/acs.analchem.7b03100","title":"Metabolomics of Small Numbers of Cells: Metabolomic Profiling of 100, 1000, and 10000 Human Breast Cancer Cells","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Multiple Sclerosis Foundation; Canada Research Chairs; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures; Genome Canada","keywords":"Chemistry; Metabolomics; Human breast; Profiling (computer programming); Breast cancer; Computational biology; Chromatography; Cancer; Internal medicine","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":[0.0002737749,0.0002479354,0.0007177353,0.00003679609,0.0001077117,0.00001728253,0.0004405694,0.0002146036,0.00008707258],"category_scores_gemma":[0.00006185098,0.0002280987,0.000234034,0.0000680782,0.0006736694,0.000006796855,0.0003633188,0.0001402694,5.240689e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001042363,"about_ca_system_score_gemma":0.00009204989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002794655,"about_ca_topic_score_gemma":0.00002445959,"domain_scores_codex":[0.998542,0.00002255604,0.0005500941,0.0004344075,0.0001665477,0.0002844331],"domain_scores_gemma":[0.9983186,0.00002356736,0.0005815285,0.0007332864,0.000227273,0.0001157695],"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":[0.00009389683,0.0001095768,0.007230673,0.0003159739,0.000505887,0.000001069081,0.0000150929,0.00001527749,0.9909323,0.000442979,0.0001320227,0.0002052256],"study_design_scores_gemma":[0.0005865098,0.00004187615,0.00371499,0.0000273014,0.0003859734,0.000004247961,0.00008724765,0.00009155485,0.9942335,0.0000904288,0.0005272165,0.0002090796],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931724,0.001827243,0.00006604214,0.00007343015,0.00006549785,0.0001035804,0.0003278345,0.000004122121,0.004359817],"genre_scores_gemma":[0.9957671,0.002001773,0.001171017,0.0000165383,0.0001212101,0.000007184392,0.00002349877,0.00002470169,0.0008669449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003515683,"threshold_uncertainty_score":0.9301596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162804750154595,"score_gpt":0.2814640108854259,"score_spread":0.2651835358699664,"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."}}