ArticleJournal of translational medicine2019
Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia.
Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease.Reviews in cardiovascular medicine · 2023Article
- Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.PLOS global public health · 2022Article
- A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data.Journal of Cancer · 2022Article
- Machine learning based on metabolomics reveals potential targets and biomarkers for primary Sjogren's syndrome.Frontiers in molecular biosciences · 2022Article
- Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.Journal of proteome research · 2021Article
- Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.BMJ open · 2020Article
- microRNA-146a Promotes Growth of Acute Leukemia Cells by Downregulating Ciliary Neurotrophic Factor Receptor and Activating JAK2/STAT3 Signaling.Yonsei medical journal · 2019Article
- PTPRU, As A Tumor Suppressor, Inhibits Cancer Stemness By Attenuating Hippo/YAP Signaling Pathway.OncoTargets and therapy · 2019Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundThe altered concentrations of amino acids were found in the bone marrow or blood of leukemia patients. Metabolomics technology combining mathematical model of biomarkers could be used for assisting the diagnosis of pediatric acute leukemia (AL).
methodsThe concentrations of 17 amino acids was measured by targeted liquid chromatograph-tandem mass spectrometry in periphery blood collected using dried blood spots. After evaluation, the mathematical models were further evaluated by prospective clinical validation cohort for AL diagnosis.
resultsThe concentrations of 13 in 17 amino acids were statistically different between the periphery blood dried serum dots measured by targeted LC-MS/MS. The receiver operating characteristic analysis for the models of amino acid panel showed that the area under curve for AL diagnosis were 0.848, 0.834 and 0.856 by SVM, RF and XGBoost. The Kappa values in further prospectively evaluated clinical cohort were 0.697, 0.703 and 0.789 (p > 0.05) respectively, and the accuracies for the models were 84.86%, 85.20% and 89.46% respectively with further clinical validation.
conclusionsThe established mathematical model is a faster, cheaper and more convenient way than conventional methods, and no significant difference on the effect of diagnosis comparing with conventional methods. The mathematical model can be clinically useful for assisting pediatric AL diagnosis.
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