Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia

Liu, Zhi-Dai; Zhou, Tingting; Han, Xing; Lang, Tingyuan; Liu, Shan; Zhang, Penghui; Liu, Haiyan; Wan, Ke-Xing; Yu, Jianhua; Zhang, Liang; Chen, Liyan; Beuerman, Roger W.; Peng, Bin; Zhou, Lei; Zou, Lin

doi:10.1186/s12967-019-1783-9

Cited by 11 publications

(12 citation statements)

References 41 publications

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“…Acute leukemia (AL), one of the most common malignant tumors in children, is classified as acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), among which ALL accounts for 60–70% and AML for 30–40% of all AL cases 12. Due to aggravated environmental and air pollution, the incidence of childhood AL has increased in recent years 3.…”

Section: Introductionmentioning

confidence: 99%

microRNA-146a Promotes Growth of Acute Leukemia Cells by Downregulating Ciliary Neurotrophic Factor Receptor and Activating JAK2/STAT3 Signaling

2019

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PurposeAcute leukemia (AL) is classified as acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). This study aimed to investigate the effect of miR-146a on childhood AL and its underlying molecular mechanisms.Materials and MethodsBone marrow samples were obtained from 39 AL children and 10 non-cancer controls. The expressions of miR-146a and ciliary neurotrophic factor receptor (CNTFR) were detected by quantitative real-time polymerase chain reaction (qRT-PCR) in ALL and AML pediatric patients, as well as ALL (Jurkat) and AML (HL-60) cells. Correlations between miR-146a and clinical indicators were explored. A targeting relationship between miR-146a and CNTFR was detected by dual luciferase reporter gene assay. Cell proliferation, apoptosis, migration, and invasion of Jurkat and HL-60 cells were measured by MTT assay, flow cytometry, and transwell assay, respectively. LIF expression was detected by qRT-PCR in Jurkat and HL-60 cells. The expression of p-JAK2, JAK2, p-STAT3, and STAT3 in HL-60 cells was measured by Western blot.ResultsmiR-146a was increased in ALL and AML pediatric patients, while CNTFR was decreased. miR-146a expression was associated with immunophenotype, karyotype, fusion gene, and SIL-TAL1. CNTFR was a target gene of miR-146a. miR-146a could promote cell proliferation, migration, and invasion, as well as inhibit cell apoptosis in Jurkat and HL-60 cells by downregulating CNTFR. Meanwhile, miR-146a inhibited the expression of LIF and activated JAK2/STAT3 pathway by downregulating CNTFR.ConclusionmiR-146a could promote the proliferation, migration, and invasion and inhibit the apoptosis of AL Jurkat and HL-60 cells by downregulating CNTFR and activating the JAK2/STAT3 pathway.

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Section: Introductionmentioning

confidence: 99%

microRNA-146a Promotes Growth of Acute Leukemia Cells by Downregulating Ciliary Neurotrophic Factor Receptor and Activating JAK2/STAT3 Signaling

2019

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“…The XGBoost model has achieved excellent performance in many fields of medical research. [23][24][25][26] Currently, no researchers have used the XGBoost model to predict the time series data of human brucellosis.…”

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confidence: 99%

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Alim

Guan

et al. 2020

BMJ Open

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ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.DesignTime-series study.SettingMainland China.MethodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.ResultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.ConclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

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“…In one example, the combination of clinical features and proteomics data of plasma samples from Alzheimer’s patients effectively predicted Alzheimer’s disease using a Support Vector Machine (SVM) classifier; the performance strongly depended on the patient’s race, reiterating the importance of including patients of different racial backgrounds in omics data sets . Machine learning and omics data have also been used to identify leukemia in children with 90% accuracy by combining the supervised classification tool, XGBoost, with LC–MS data quantifying amino acids . In another example, lipidomics data were analyzed with LASSO feature selection and an SVM classifier to accurately classify patients with renal cancer .…”

Section: Introductionmentioning

confidence: 99%

“…More recent supervised classification strategies that leverage the power and speed of modern computing, including Support Vector Machine (SVM) and decision tree-based classifiers, such as XGBoost, show even greater promise for omics researchers over these older methods. In several examples of studies using mass spectrometry data, SVM was the method of choice for performing supervised classification; , this approach outperformed methods such as linear discriminant analysis and partial least-squares discriminant analysis on multiple proteomics data sets. , Decision-tree-based classifiers, including Random Forest, boosted decision trees, and XGBoost, have shown similar promise for accurate classification of omics data. ,, The development of new and better classifiers provides new opportunities to do a better job of supervised classification, which translates into an enhanced ability to discriminate disease and ultimately improve health outcomes.…”

Section: Introductionmentioning

confidence: 99%

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

Hua

Desaire

2021

J. Proteome Res.

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Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand. While mass spectrometry data sets are rich with potentially useful information, using the data effectively can be challenging because of missing entries in the data sets and because the number of samples is typically much smaller than the number of features, two challenges that make machine learning difficult. To address this problem, we have modified a new supervised classification tool, the Aristotle Classifier, so that omics data sets can be better leveraged for identifying disease states. The optimized classifier, AC.2021, is benchmarked on multiple data sets against its predecessor and two leading supervised classification tools, Support Vector Machine (SVM) and XGBoost. The new classifier, AC.2021, outperformed existing tools on multiple tests using proteomics data. The underlying code for the classifier, provided herein, would be useful for researchers who desire improved classification accuracy when using their omics data sets to identify disease states.

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Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia

Cited by 11 publications

References 41 publications

microRNA-146a Promotes Growth of Acute Leukemia Cells by Downregulating Ciliary Neurotrophic Factor Receptor and Activating JAK2/STAT3 Signaling

microRNA-146a Promotes Growth of Acute Leukemia Cells by Downregulating Ciliary Neurotrophic Factor Receptor and Activating JAK2/STAT3 Signaling

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

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