1. Integration of ultra-high-pressure liquid chromatography–tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke
- Author
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Ao Qi, Chun-yang Zhang, Lulu Liu, Junjie Zhang, Yusen Chen, Chun Cai, Lijian Zhang, Qisheng Zhong, Fei Ma, and Simin Xu
- Subjects
Male ,Metabolite ,Machine learning ,computer.software_genre ,Mass spectrometry ,Catalysis ,Brain Ischemia ,Machine Learning ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Tandem Mass Spectrometry ,Liquid chromatography–mass spectrometry ,Materials Chemistry ,Humans ,Medicine ,cardiovascular diseases ,Ultra high pressure ,Stroke ,Chromatography, High Pressure Liquid ,Aged ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,business.industry ,Fatty Acids ,Metals and Alloys ,Fatty acid ,General Chemistry ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Surfaces, Coatings and Films ,Electronic, Optical and Magnetic Materials ,chemistry ,Ischemic stroke ,Ceramics and Composites ,Female ,Artificial intelligence ,business ,computer ,Biomarkers ,030217 neurology & neurosurgery - Abstract
We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke. In particular, we develop an optimal model to discriminate ischemic stroke patients from healthy persons with 100% sensitivity and 93.18% specificity. This research may facilitate understanding the roles of fatty acid metabolites in stroke occurrence, holding great potential in clinical stroke diagnosis.
- Published
- 2020
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