Analysis Strategy of Deep Vein Thrombosis Metabolomic Biomarkers Based on Machine Learning Algorithms
10.19756/j.issn.0253-3820.231242
- VernacularTitle:基于机器学习算法的深静脉血栓代谢标志物的分析策略
- Author:
Ming-Feng LIU
1
;
Yan-Juan WU
;
Shi-Dong ZHOU
;
Li-Hong DANG
;
Jian LI
;
Yan DU
;
Jun-Hong SUN
;
Jie CAO
Author Information
1. 山西医科大学法医学院，晋中030600
- Keywords:
Deep vein thrombosis;
Machine learning;
Metabolomics;
Gas chromatography-mass spectrometry;
Feature selection
- From:
Chinese Journal of Analytical Chemistry
2024;52(7):1039-1049,后插1-后插4,封3
- CountryChina
- Language:Chinese
-
Abstract:
Deep vein thrombosis(DVT)is a common peripheral vascular disease in clinical practice.The lack of precise and efficient early diagnostic techniques renders it susceptible to being overlooked or misdiagnosed,and therefore,identifying trustworthy biomarkers is a major issue that has to be resolved.In this study,the endogenous metabolites in the urine of DVT rats were screened by metabolomics technology based on gas chromatograph-mass spectrometry(GC-MS)and the characteristic metabolites were identified by multiple feature selection algorithms and multivariate statistical analysis,for the development of a machine learning-based diagnostic model for DVT.The urine samples in metabolic cage in the thrombus development phase(between 48 and 72 h)of rats were collected,which was used as the models for inferior vena cava ligation.The metabolic profiles of the control group and DVT were obtained using the GC-MS method.A total of 176 kinds of endogenous metabolites were identified in rat urine through comparison with the FiehnLib database,26 kinds of differential metabolites associated with DVT were screened through a combination of the Mann-Whitney U test and orthogonal partial least squares discriminant analysis(OPLS-DA),and 13 kinds of significant metabolites strongly correlated with DVT were further evaluated in conjunction with various machine learning feature selection techniques.For DVT diagnosis,machine learning models such as Gaussian Naive Bayes(GNB),support vector machine(SVM),logistic regression(LR),and linear discriminant analysis(LDA)were developed.The diagnostic model constructed using 13 kinds of key metabolites demonstrated excellent accuracy and stability,and surpassed the predictive performance of the models utilizing 176 kinds of metabolites and 26 kinds of differential metabolites,as evidenced by examination and comparison of each model's efficacy.The study showed that the integration of multiple feature selection algorithms for analyzing metabolite information in DVT rat urine was capable of effectively identifying reliable potential markers of DVT.Furthermore,the developed machine learning model offered a novel technical approach for the automated diagnosis of DVT.
