TY - CHAP
T1 - Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS
AU - Chen, Yongnan
AU - Theofilatos, Konstantinos
AU - Papageorgiou, Lazaros G.
AU - Tsoka, Sophia
PY - 2020/5/22
Y1 - 2020/5/22
N2 - 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.
AB - 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.
KW - Acute ischemic stroke
KW - Biological pathway
KW - Gene expression profile
KW - Machine learning
KW - Mathematical programming
KW - Microarray
KW - MILP optimisation
UR - http://www.scopus.com/inward/record.url?scp=85092657159&partnerID=8YFLogxK
U2 - 10.1145/3405758.3405767
DO - 10.1145/3405758.3405767
M3 - Conference paper
AN - SCOPUS:85092657159
T3 - ACM International Conference Proceeding Series
SP - 25
EP - 31
BT - Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020
PB - Association for Computing Machinery
T2 - 12th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2020
Y2 - 22 May 2020 through 24 May 2020
ER -