TY - GEN
T1 - Identifying disease network perturbations through regression on gene expression and pathway topology analysis
AU - Dimitrakopoulos, Georgios N.
AU - Balomenos, Panos
AU - Vrahatis, Aristidis G.
AU - Sgarbas, Kyriakos
AU - Bezerianos, Anastasios
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85009065154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009065154&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7592088
DO - 10.1109/EMBC.2016.7592088
M3 - Conference contribution
C2 - 28269612
AN - SCOPUS:85009065154
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 5969
EP - 5972
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -