Journal article
Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model
IEEE/ACM transactions on computational biology and bioinformatics, v 17(6), pp 2162-2169
Nov 2020
PMID: 31247559
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The development and prognosis of complex diseases usually involves changes in regulatory relationships among biomolecules. Understanding how the regulatory relationships change with genetic alterations can help to reveal the underlying biological mechanisms for complex diseases. Although several models have been proposed to estimate the differential network between two different states, they are not suitable to deal with situations where the molecules of interest are affected by other covariates. Nor can they make use of prior information that provides insights about the structures of biomolecular networks. In this study, we introduce a novel weighted fused conditional Gaussian graphical model to jointly estimate two state-specific biomolecular regulatory networks and their difference between two different states. Unlike previous differential network estimation methods, our model can take into account the related covariates and the prior network information when inferring differential networks. The effectiveness of our proposed model is first evaluated based on simulation studies. Experiment results demonstrate that our model outperforms other state-of-the-art differential networks estimation models in all cases. We then apply our model to identify the differential gene network between two subtypes of glioblastoma based on gene expression and miRNA expression data. Our model is able to discover known mechanisms of glioblastoma and provide interesting predictions.
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Details
- Title
- Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model
- Creators
- Le Ou-Yang - Shenzhen UniversityXiao-Fei Zhang - Central China Normal UniversityXiaohua Hu - Drexel UniversityHong Yan - City University of Hong Kong
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 17(6), pp 2162-2169
- Publisher
- IEEE
- Grant note
- CCNU18TS026 / Fundamental Research Funds for the Central Universities (10.13039/501100012226) 2016A030313710; 2015A030313624 / Natural Science Foundation of Guangdong Province (10.13039/501100003453) 201607010170 / Science and Technology Program of Guangzhou ZRMS2018001337 / Natural Science Foundation of Hubei province (10.13039/501100003819) JCYJ20170817095210760 / Shenzhen Fundamental Research Program (10.13039/501100017607) 61602309; 11871026; 61402190; 61532008 / National Natural Science Foundation of China (10.13039/501100001809) CityU 11200818 / Research Grants Council of Hong Kong 2017077 / Natural Science Foundation of SZU
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000597841800031
- Scopus ID
- 2-s2.0-85097577637
- Other Identifier
- 991019167578604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematics, Interdisciplinary Applications
- Statistics & Probability