Cited By
View all- Chen YLiu SPapageorgiou LTheofilatos KTsoka S(2023)Optimisation Models for Pathway Activity Inference in CancerCancers10.3390/cancers1506178715:6(1787)Online publication date: 15-Mar-2023
Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS
Authors: 
Yongnan Chen, Konstantinos Theofilatos, Lazaros G. Papageorgiou, Sophia TsokaAuthors Info & Claims
Pages 25 - 31
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.
References
[1]
Wang, P.L., Zhao, X.Q., Yang, Z.H., Wang, A.X., Wang, C.X., Liu, L.P., Wang, Y.L., Wang, X.G., Yi, J.U., Chen, S.Y. and Chen, Q.D., 2012. Effect of in-hospital medical complications on case fatality post-acute ischemic stroke: data from the China National Stroke Registry. Chinese medical journal, 125(14), pp. 2449--2454.
[2]
Johnson, C.O., Nguyen, M., Roth, G.A., Nichols, E., Alam, T., Abate, D., Abd-Allah, F., Abdelalim, A., Abraha, H.N., Abu-Rmeileh, N.M. and Adebayo, O.M., 2019. Global, regional, and national burden of stroke, 1990--2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 18(5), pp. 439--458.
[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.
[4]
Krug, T., Gabriel, J.P., Taipa, R., Fonseca, B.V., Domingues-Montanari, S., Fernandez-Cadenas, I., Manso, H., Gouveia, L.O., Sobral, J., Albergaria, I. and Gaspar, G., 2012. TTC7B emerges as a novel risk factor for ischemic stroke through the convergence of several genome-wide approaches. Journal of Cerebral Blood Flow & Metabolism, 32(6), pp. 1061--1072.
[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.
[6]
Joshi-Tope, G., Gillespie, M., Vastrik, I., D'Eustachio, P., Schmidt, E., de Bono, B., Jassal, B., Gopinath, G.R., Wu, G.R., Matthews, L. and Lewis, S., 2005. Reactome: a knowledgebase of biological pathways. Nucleic acids research, 33(suppl_1), pp.D428-D432.
[7]
Kanehisa, M., 2002, January. The KEGG database. In Novartis Foundation Symposium (pp. 91-100). Chichester; New York; John Wiley; 1999.
[8]
Gene Ontology Consortium, 2004. The Gene Ontology (GO) database and informatics resource. Nucleic acids research, 32(suppl_1), pp.D258-D261.
[9]
Barr, T.L., Conley, Y., Ding, J., Dillman, A., Warach, S., Singleton, A. and Matarin, M., 2010. Genomic biomarkers and cellular pathways of ischemic stroke by RNA gene expression profiling. Neurology, 75(11), pp. 1009--1014.
[10]
Stamova, B., Jickling, G.C., Ander, B.P., Zhan, X., Liu, D., Turner, R., Ho, C., Khoury, J.C., Bushnell, C., Pancioli, A. and Jauch, E.C., 2014. Gene expression in peripheral immune cells following cardioembolic stroke is sexually dimorphic. PloS one, 9(7).
[11]
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P. and Mesirov, J.P., 2011. Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27(12), pp. 1739--1740.
[12]
GAMS Development Corporation, 2013. General Algebraic Modeling System (GAMS), rel. 24.2. 1.
[13]
Bradley, A.P., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), pp. 1145--1159.
[14]
Guo, Z., Zhang, T., Li, X., Wang, Q., Xu, J., Yu, H., Zhu, J., Wang, H., Wang, C., Topol, E.J. and Wang, Q., 2005. Towards precise classification of cancers based on robust gene functional expression profiles. BMC bioinformatics, 6(1), p.58.
[15]
Diao, H., Li, X., Hu, S. and Liu, Y., 2012. Gene expression profiling combined with bioinformatics analysis identify biomarkers for Parkinson disease. PloS one, 7(12).
[16]
Lee, E., Chuang, H.Y., Kim, J.W., Ideker, T. and Lee, D., 2008. Inferring pathway activity toward precise disease classification. PLoS computational biology, 4(11).
[17]
Yilmaz, G. and Granger, D.N., 2008. Cell adhesion molecules and ischemic stroke. Neurological research, 30(8), pp. 783--793.
[18]
Wu, D., Lee, Y.C.G., Liu, H.C., Yuan, R.Y., Chiou, H.Y., Hung, C.H. and Hu, C.J., 2013. Identification of TLR downstream pathways in stroke patients. Clinical biochemistry, 46(12), pp. 1058--1064.
Index Terms
- Identification of Important Biological Pathways for Ischemic Stroke Prediction through a Mathematical Programming Optimisation Model-DIGS
Recommendations
Transcriptome Data Visualization in Pathways with Application to Zebrafish Embryo Datasets
CISIS '14: Proceedings of the 2014 Eighth International Conference on Complex, Intelligent and Software Intensive SystemsGene expression data analysis from high throughput approaches is complicated due to its abundant scales. It is especially difficult to analyze and categorize temporal differential expression of gene groups and discover interactive relationship among ...
Biological pathways as features for microarray data classification
DTMBIO '08: Proceedings of the 2nd international workshop on Data and text mining in bioinformaticsClassification using microarray gene expression data is an important task in bioinformatics. Due to the high dimensionality and small sample size that characterizes microarray data, there has recently been a drive to incorporate any available ...
Identifying regulatory relationships among genomic loci, biological pathways, and disease
Objective: Elucidating genetic factors of complex diseases is one of the most important challenges in biomedical research. Recently, a genetical genomics approach of mapping genotype to transcripts has been used in complex disease analysis. This ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2020
163 pages
ISBN:9781450375719
DOI:10.1145/3405758
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
In-Cooperation
- NWPU: Northwestern Polytechnical University
- Universidade Nova de Lisboa
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICBBT 2020
ICBBT 2020: 2020 12th International Conference on Bioinformatics and Biomedical Technology
May 22 - 24, 2020
Xi'an, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 56Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025
Other Metrics
Citations
Cited By
View all- Chen YLiu SPapageorgiou LTheofilatos KTsoka S(2023)Optimisation Models for Pathway Activity Inference in CancerCancers10.3390/cancers1506178715:6(1787)Online publication date: 15-Mar-2023
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in