期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 5, 页码 2894-2906出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3103407
关键词
Covariance matrices; Proteins; Gene expression; Biology; Biological system modeling; Matrices; Estimation; Gene co-expression network inference; Gaussian graphical models; modular structures; protein-protein interaction networks
类别
资金
- National Natural Science Foundation of China [11871026, 61602309, 61532008]
- Hubei Provincial Science and Technology Innovation Base (Platform) Special Project [2020DFH002]
- Hong Kong Research Grants Council [11200818]
- Innovation and Technology Commission of Hong Kong
In this study, a novel method called prior network-dependent gene network inference (pGNI) is proposed to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The method successfully captures the modular structures in the networks and is demonstrated to be effective through simulation studies and real datasets.
Inferring gene co-expression networks from high-throughput gene expression data is an important task in bioinformatics. Many gene networks often exhibit modular structures. Although several Gaussian graphical model-based methods have been developed to estimate gene co-expression networks by incorporating the modular structural prior, none of them takes into account the modular structures captured by the prior networks (e.g., protein interaction networks). In this study, we propose a novel prior network-dependent gene network inference (pGNI) method to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The underlying modular structure is learned from both sets of data. Through simulation studies, we demonstrate the feasibility and effectiveness of our method. We also apply our method to two real datasets. The modular structures in the networks estimated by our method are biological significant.
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