Paper Info
Title
An integrative framework for protein interaction network and methylation data to discover epigenetic modules.
Abstract
DNA methylation is a critical epigenetic modification that plays an important role in cancers. The available algorithms fail to fully characterize epigenetic modules. To address this issue, we first characterize the epigenetic module as a group of well-connected genes in the protein interaction network and are also co-methylated based on gene methylation profiles. Then, the epigenetic module discovery problem is transformed into an optimization problem. Then, a regularized nonnegative matrix factorization algorithm for methylation modules ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RNMF-MM</italic> ) is presented, where the co-methylation constraint is treated as a regularizer. Using the artificial networks with known module structure, we demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of accuracy. On the basis of breast cancer methylation data and protein interaction network, the RNMF-MM algorithm discovers methylation modules that are significantly more enriched by the known pathways than those obtained by other algorithms. These modules serve as biomarkers for predicting cancer stages and estimating survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of protein interaction network and methylation data.
Year
DOI
Venue
2019
10.1109/TCBB.2018.2831666
IEEE/ACM transactions on computational biology and bioinformatics
Keywords
DocType
Volume
Proteins,DNA,Bioinformatics,Prediction algorithms,Gene expression,Breast cancer
Journal
16
Issue
ISSN
Citations 
6
1545-5963
1
PageRank 
References 
Authors
0.36
0
3
Name
Order
Citations
PageRank
Xiaoke Ma17611.69
Peng Gang Sun2997.76
Zhong-Yuan Zhang310.70