Optimised Selection of Stroke Biomarker Based on Svm and Information Theory
With the development of molecular biology and gene-engineering technology, gene diagnosis has been an emerging approach for modern life sciences. Biological marker, recognized as the hot topic in the molecular and gene fields, has important values in early diagnosis, malignant tumor stage, treatment...
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EDP Sciences
2017-01-01
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Series: | ITM Web of Conferences |
Online Access: | https://doi.org/10.1051/itmconf/20171205013 |
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doaj-68e24341d63947af91e290335ffdd70f2021-02-02T00:54:48ZengEDP SciencesITM Web of Conferences2271-20972017-01-01120501310.1051/itmconf/20171205013itmconf_ita2017_05013Optimised Selection of Stroke Biomarker Based on Svm and Information TheoryWang XiangShi WeiWang Xiao-CuiWang TaoWith the development of molecular biology and gene-engineering technology, gene diagnosis has been an emerging approach for modern life sciences. Biological marker, recognized as the hot topic in the molecular and gene fields, has important values in early diagnosis, malignant tumor stage, treatment and therapeutic efficacy evaluation. So far, the researcher has not found any effective way to predict and distinguish different type of stroke. In this paper, we aim to optimize stroke biomarker and figure out effective stroke detection index based on SVM (support vector machine) and information theory. Through mutual information analysis and principal component analysis to complete the selection of biomarkers and then we use SVM to verify our model. According to the testing data of patients provided by Xuanwu Hospital, we explore the significant markers of the stroke through data analysis. Our model can predict stroke well. Then discuss the effects of each biomarker on the incidence of stroke.https://doi.org/10.1051/itmconf/20171205013 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wang Xiang Shi Wei Wang Xiao-Cui Wang Tao |
spellingShingle |
Wang Xiang Shi Wei Wang Xiao-Cui Wang Tao Optimised Selection of Stroke Biomarker Based on Svm and Information Theory ITM Web of Conferences |
author_facet |
Wang Xiang Shi Wei Wang Xiao-Cui Wang Tao |
author_sort |
Wang Xiang |
title |
Optimised Selection of Stroke Biomarker Based on Svm and Information Theory |
title_short |
Optimised Selection of Stroke Biomarker Based on Svm and Information Theory |
title_full |
Optimised Selection of Stroke Biomarker Based on Svm and Information Theory |
title_fullStr |
Optimised Selection of Stroke Biomarker Based on Svm and Information Theory |
title_full_unstemmed |
Optimised Selection of Stroke Biomarker Based on Svm and Information Theory |
title_sort |
optimised selection of stroke biomarker based on svm and information theory |
publisher |
EDP Sciences |
series |
ITM Web of Conferences |
issn |
2271-2097 |
publishDate |
2017-01-01 |
description |
With the development of molecular biology and gene-engineering technology, gene diagnosis has been an emerging approach for modern life sciences. Biological marker, recognized as the hot topic in the molecular and gene fields, has important values in early diagnosis, malignant tumor stage, treatment and therapeutic efficacy evaluation. So far, the researcher has not found any effective way to predict and distinguish different type of stroke. In this paper, we aim to optimize stroke biomarker and figure out effective stroke detection index based on SVM (support vector machine) and information theory. Through mutual information analysis and principal component analysis to complete the selection of biomarkers and then we use SVM to verify our model. According to the testing data of patients provided by Xuanwu Hospital, we explore the significant markers of the stroke through data analysis. Our model can predict stroke well. Then discuss the effects of each biomarker on the incidence of stroke. |
url |
https://doi.org/10.1051/itmconf/20171205013 |
work_keys_str_mv |
AT wangxiang optimisedselectionofstrokebiomarkerbasedonsvmandinformationtheory AT shiwei optimisedselectionofstrokebiomarkerbasedonsvmandinformationtheory AT wangxiaocui optimisedselectionofstrokebiomarkerbasedonsvmandinformationtheory AT wangtao optimisedselectionofstrokebiomarkerbasedonsvmandinformationtheory |
_version_ |
1724312709510463488 |