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Open Access Green as soon as Postprint is submitted to ZB.
Bayesian inference of latent causes in gene regulatory dynamics.
In: Proceedings (10th international conference on Latent Variable Analysis and Signal Separation). Berlin [u.a.]: Springer, 2012. 520-527 (Lect. Notes Comput. Sc. ; 7191)
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.
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Publication type
Article: Conference contribution
Editors
Theis, F.J. ; Cichocki, A.* ; Yeredor, A* ; Zibulevsky, M.*
Keywords
Independent component analysis; Bayesian inference; latent causes
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
ISBN
978-3-642-28550-9
Conference Title
10th international conference on Latent Variable Analysis and Signal Separation
Proceedings Title
Proceedings
Quellenangaben
Volume: 7191,
Pages: 520-527
Series
Lecture Notes in Computer Science
Publisher
Springer
Publishing Place
Berlin [u.a.]
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