Paper Info
Title
Gene regulatory network inference and validation using relative change ratio analysis and time-delayed dynamic Bayesian network.
Abstract
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our approach were evaluated comparatively along with 29 other submissions by two metrics (area under the ROC curve and area under the precision-recall curve). The overall performance of our approach ranked the second among all participating teams.
Year
DOI
Venue
2014
10.1186/s13637-014-0012-3
EURASIP J. Bioinformatics and Systems Biology
Keywords
DocType
Volume
Dialogue for Reverse Engineering Assessments and Methods (DREAM),Gene regulatory network (GRN),Relative change ratio (RCR),Time-delayed dynamic Bayesian network (TDBN)
Journal
2014
Issue
ISSN
Citations 
1
1687-4153
3
PageRank 
References 
Authors
0.39
8
6
Name
Order
Citations
PageRank
Peng Li130.39
Gong P213317.18
Haoni Li341.13
Edward J. Perkins422520.46
Nan Wang561.17
Chaoyang Zhang623022.23