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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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Publikationstyp
Artikel: Konferenzbeitrag
Herausgeber
Theis, F.J. ; Cichocki, A.* ; Yeredor, A* ; Zibulevsky, M.*
Schlagwörter
Independent component analysis; Bayesian inference; latent causes
Sprache
englisch
Veröffentlichungsjahr
2012
HGF-Berichtsjahr
0
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
ISBN
978-3-642-28550-9
Konferenztitel
10th international conference on Latent Variable Analysis and Signal Separation
Konferenzband
Proceedings
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 7191,
Seiten: 520-527
Reihe
Lecture Notes in Computer Science
Verlag
Springer
Verlagsort
Berlin [u.a.]
POF Topic(s)
30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-503700-004
Erfassungsdatum
2012-10-29