期刊论文

Zhang, XF

英文

IEEE/ACM Transactions on Computational Biology and Bioinformatics

1545-5963

2022

19

1

513-521

10.1109/TCBB.2020.3002906

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 11871026, 61402190, 61602309 and 61532008) 10.13039/501100003819-Natural Science Foundation of Hubei province (Grant Number: 2018CFB521) 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: CCNU18TS026) 10.13039/501100017622-Shenzhen Research and Development program (Grant Number: JCYJ20170817095210760) Natural Science Foundation of SZU (Grant Number: 2017077) Science and Technology Program of Guangzhou (Grant Number: 201607010170) Hong Kong Research Grants Council (Grant Number: C1007-15G and 11200818)

本校为第一且通讯机构

It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorp...