Mean-field approaches to independent component analysis
Neural Computation
Estimating Hidden Influences in Metabolic and Gene Regulatory Networks
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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In the study of gene regulatory networks, more and more quantitative data becomes available. However, few of the players in such networks are observed, others are latent. Focusing on the inference of multiple such latent causes, we arrive at a blind source separation problem. Under the assumptions of independent sources and Gaussian noise, this condenses to a Bayesian independent component analysis problem with a natural dynamic structure. We here present a method for the inference in networks with linear dynamics, with a straightforward extension to the nonlinear case. The proposed method uses a maximum a posteriori estimate of the latent causes, with additional prior information guaranteeing independence. We illustrate the feasibility of our method on a toy example and compare the results with standard approaches.