Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS-Reference-Cited by-同舟云学术

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

Published:2020-05-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology
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Author:

Chen Yongnan¹,Theofilatos Konstantinos²,Papageorgiou Lazaros G.³,Tsoka Sophia¹

Affiliation:

1. Dept of Informatics, King's College London, London, UK

2. School of Cardiovascular Medicine & Science, King's College London, London, UK

3. Dept of Chemical Engineering, University College London, London, UK

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3405758.3405767

Reference18 articles.

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2. Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

3. Theofilatos K. Korfiati A. Mavroudi S. Cowperthwaite M.C. and Shpak M. 2019. Discovery of stroke-related blood biomarkers from gene expression network models. BMC medical genomics 12(1) p.118. Theofilatos K. Korfiati A. Mavroudi S. Cowperthwaite M.C. and Shpak M. 2019. Discovery of stroke-related blood biomarkers from gene expression network models. BMC medical genomics 12(1) p.118.

4. TTC7B Emerges as a Novel Risk Factor for Ischemic Stroke Through the Convergence of Several Genome-Wide Approaches

5. Yang L. Ainali C. Tsoka S. and Papageorgiou L.G. 2014. Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. BMC bioinformatics 15(1) p.390. Yang L. Ainali C. Tsoka S. and Papageorgiou L.G. 2014. Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. BMC bioinformatics 15(1) p.390.

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