{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001786787,0.0002561221,0.0002656448,0.0003111846,0.0001887636,0.0002574071,0.0001549044,0.0003273442,0.001508124],"category_scores_gemma":[0.0001567758,0.0001104842,0.0001418404,0.0003789502,0.0001299557,0.0001856283,0.0001898408,0.0002865009,0.0004635597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002020872,"about_ca_system_score_gemma":0.0001471524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000954996,"about_ca_topic_score_gemma":0.001763358,"domain_scores_codex":[0.9998586,0.00001965607,0.00001265772,0.00003496476,0.00005769984,0.00001645868],"domain_scores_gemma":[0.9999465,0.00001640407,0.000004457476,0.000006357715,0.00001582972,0.0000105327],"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":[0.00005466938,0.00001813191,0.0005501533,0.0000212571,0.000003354397,0.00001330406,0.00001032889,0.00005533358,0.9970235,0.0000171813,0.00004958373,0.00218315],"study_design_scores_gemma":[0.00001571881,0.0002605461,0.02885409,0.000007355494,0.00002306681,0.0001761871,0.00005566419,0.002494156,0.9653862,0.00006914558,0.002648635,0.000009308997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722952,0.001680831,0.02136459,0.0002669657,0.00003222855,0.0001017171,0.001674349,0.0001491894,0.002434882],"genre_scores_gemma":[0.9330527,0.002216496,0.05205109,0.0002385882,0.00002126497,0.0004360924,0.006275582,0.00005954594,0.005648794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001508124,"threshold_uncertainty_score":0.005045176,"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."}}