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Verfasst von:Böck, Matthias [VerfasserIn]   i
 Kaderali, Lars [VerfasserIn]   i
Titel:Hub-centered gene network reconstruction using automatic relevance determination
Verf.angabe:Matthias Böck, Soichi Ogishima, Hiroshi Tanaka, Stefan Kramer, Lars Kaderali
E-Jahr:2012
Jahr:May 3, 2012
Umfang:17 S.
Teil:volume:7
 year:2012
 number:5
 elocationid:e35077
 extent:17
Fussnoten:Gesehen am 19.10.2018
Titel Quelle:Enthalten in: PLOS ONE
Ort Quelle:San Francisco, California, US : PLOS, 2006
Jahr Quelle:2012
Band/Heft Quelle:7(2012), 5, Artikel-ID e35077
ISSN Quelle:1932-6203
Abstract:Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.
DOI:doi:10.1371/journal.pone.0035077
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext ; Verlag: http://dx.doi.org/10.1371/journal.pone.0035077
 kostenfrei: Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0035077
 DOI: https://doi.org/10.1371/journal.pone.0035077
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Cell cycle and cell division
 Gene regulation
 Gene regulatory networks
 Genetic networks
 Markov models
 Microarrays
 Scale-free networks
 Simulation and modeling
K10plus-PPN:1582105898
Verknüpfungen:→ Zeitschrift

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