Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Analyzing time series gene expression data
Bioinformatics
Multicriteria gene screening for analysis of differential expression with DNA microarrays
EURASIP Journal on Applied Signal Processing
Journal of Computational Neuroscience
Non-stationary bayesian networks based on perfect simulation
Proceedings of the 21st ACM international conference on Information and knowledge management
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Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.