(2016). Identifying disease network perturbations through regression on gene expression and pathway topology analysis. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5969–5972). IEEE. https://doi.org/10.1109/embc.2016.7592088
Περίληψη
In Systems Biology, network-based approaches have been extensively used to effectively study complex diseases. An important challenge is the detection of network perturbations which disrupt regular biological functions as a result of a disease. In this regard, we introduce a network based pathway analysis method which isolates casual interactions with significant regulatory roles within diseased-perturbed pathways. Specifically, we use gene expression data with Random Forest regression models to assess the interactivity strengths of genes within disease-perturbed networks, using KEGG pathway maps as a source of prior-knowledge pertaining to pathway topology. We deliver as output a network with imprinted perturbations corresponding to the biological phenomena arising in a disease-oriented experiment. The efficacy of our approach is demonstrated on a serous papillary ovarian cancer experiment and results highlight the functional roles of high impact interactions and key gene regulators which cause strong perturbations on pathway networks, in accordance with experimentally validated knowledge from recent literature.
- DOI
- 10.1109/embc.2016.7592088
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2016
Σύνδεσμοι
BibTeX
@inproceedings{dimitrakopoulos2016identifying,
title = {Identifying disease network perturbations through regression on gene expression and pathway topology analysis},
author = {Georgios N. Dimitrakopoulos and Panos Balomenos and Aristidis G. Vrahatis and Kyriakos Sgarbas and Anastasios Bezerianos},
url = {https://doi.org/10.1109/embc.2016.7592088},
doi = {10.1109/embc.2016.7592088},
year = {2016},
date = {2016-01-01},
booktitle = {2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
volume = {5},
pages = {5969–5972},
publisher = {IEEE},
abstract = {In Systems Biology, network-based approaches have been extensively used to effectively study complex diseases. An important challenge is the detection of network perturbations which disrupt regular biological functions as a result of a disease. In this regard, we introduce a network based pathway analysis method which isolates casual interactions with significant regulatory roles within diseased-perturbed pathways. Specifically, we use gene expression data with Random Forest regression models to assess the interactivity strengths of genes within disease-perturbed networks, using KEGG pathway maps as a source of prior-knowledge pertaining to pathway topology. We deliver as output a network with imprinted perturbations corresponding to the biological phenomena arising in a disease-oriented experiment. The efficacy of our approach is demonstrated on a serous papillary ovarian cancer experiment and results highlight the functional roles of high impact interactions and key gene regulators which cause strong perturbations on pathway networks, in accordance with experimentally validated knowledge from recent literature.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}