Analyzing Gene Expression Time-Courses
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Analyzing time series gene expression data
Bioinformatics
Inferring gene regulatory networks from temporal expression profiles under time-delay and noise
Computational Biology and Chemistry
The wavelet-based cluster analysis for temporal gene expression data
EURASIP Journal on Bioinformatics and Systems Biology
Robust cross-race gene expression analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
| Hi-index | 0.00 |
Inferring gene networks from longitudinal gene expression microarrays is a crucial step towards the study of gene regulatory mechanisms. A decade ago, expensive microarray technology restricted the number of samples undergoing gene expression profiling in single studies, leading the inference algorithms that assume stationary gene networks to the best solution. Thanks to decreasing cost of modern microarray technologies, more gene expression profiles can be assessed in single studies. With more samples available, we can relax the stationarity assumption and develop a method to infer dynamic gene networks, which can reflect more realistic biology where genes adaptively orchestrate each other. This paper applied the framework of dynamic Bayesian networks to infer adaptive gene interactions by identifying individual transition networks between pairs of consecutive times. Due to high computational burden of inferring the interconnection patterns among all genes over time, we designed a parallelizable inference algorithm to make feasible the task. We validated our approach by two clinical studies: yellow fever vaccination and mechanical periodontal therapy. The inferred dynamic networks achieved more than 90% predictive accuracy, a significant improvement when compared to stationary models (p驴