Abstract
Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.
| Original language | English |
|---|---|
| Pages (from-to) | 111-121 |
| Number of pages | 11 |
| Journal | Computers in Biology and Medicine |
| Volume | 51 |
| DOIs | |
| Publication status | Published - Aug 1 2014 |
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Keywords
- Cancer
- Computational method
- Gene expression
- Protein-protein interaction
- Renal cell carcinoma
- Signaling pathway
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics
- Medicine(all)
Cite this
Revealing pathway maps of renal cell carcinoma by gene expression change. / Hung, Fei Hung; Chiu, Hung Wen; Chang, Yo-Cheng.
In: Computers in Biology and Medicine, Vol. 51, 01.08.2014, p. 111-121.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Revealing pathway maps of renal cell carcinoma by gene expression change
AU - Hung, Fei Hung
AU - Chiu, Hung Wen
AU - Chang, Yo-Cheng
PY - 2014/8/1
Y1 - 2014/8/1
N2 - Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.
AB - Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.
KW - Cancer
KW - Computational method
KW - Gene expression
KW - Protein-protein interaction
KW - Renal cell carcinoma
KW - Signaling pathway
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UR - http://www.scopus.com/inward/citedby.url?scp=84901840263&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2014.04.023
DO - 10.1016/j.compbiomed.2014.04.023
M3 - Article
C2 - 24907414
AN - SCOPUS:84901840263
VL - 51
SP - 111
EP - 121
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
ER -