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Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationProceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020
PublisherAssociation for Computing Machinery
Pages25-31
Number of pages7
ISBN (Electronic)9781450375719
DOIs
Published22 May 2020
Event12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020 - Xi'an, China
Duration: 22 May 202024 May 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020
CountryChina
CityXi'an
Period22/05/202024/05/2020

King's Authors

Abstract

Stroke ranks second after heart disease as a cause of disability in high-income countries and as a cause of death worldwide. Identifying the biomarkers of ischemic stroke is possible to help diagnose stroke cases from non-stroke cases, as well as advancing the understanding of the underlying theory of the disease. In this study, a mathematical programming optimisation framework called DIGS is applied to build a phenotype classification and significant pathway inference model using stroke gene expression profile data. DIGS model is specifically designed for pathway activity inference towards supervised multi-class disease classification and is proved has great performance among the mainstream pathway activity inference methods. The highest accuracy of the prediction on determining stroke or non-stroke samples reaches 84.4% in this work, which is much better than the prediction accuracy produced by currently found stroke gene biomarkers. Also, stroke-related significant pathways are inferred from the outputs of DIGS model in this work. Taken together, the combination of DIGS model and expression profiles of stroke has better performance on the discriminate power of sample phenotypes and is capable of effective in-depth analysis on the identification of biomarkers.

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