- Record: found
- Abstract: found
- Article: not found
Integration of ultra-high-pressure liquid chromatography–tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke
Lijian Zhang
1
,
2
,
3
,
4
,
5 ,
Fei Ma
6
,
7
,
8
,
9
,
10 ,
Ao Qi
1
,
2
,
3
,
4
,
5 ,
Lulu Liu
1
,
2
,
3
,
4
,
5 ,
Junjie Zhang
1
,
2
,
3
,
4
,
5 ,
Simin Xu
1
,
2
,
3
,
4
,
5 ,
Qisheng Zhong
11
,
12
,
13 ,
Yusen Chen
1
,
2
,
3
,
4
,
5 ,
Chun-yang Zhang
6
,
7
,
8
,
9
,
10 ,
Chun Cai
1
,
2
,
3
,
4
,
5
2020
Read this article at
ScienceOpenPublisher
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
We demonstrated the integration of UHPLC–MS/MS with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke.
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.