Downloads: 125

Files in This Item:
File Description SizeFormat 
scr_2013_75.pdf70.99 kBAdobe PDFView/Open
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTimothy, Hancocken
dc.date.accessioned2013-05-17T04:17:55Z-
dc.date.available2013-05-17T04:17:55Z-
dc.date.issued2013-03-
dc.identifier.urihttp://hdl.handle.net/2433/173972-
dc.description平成24年度 京都大学化学研究所 スーパーコンピュータシステム 利用報告書ja
dc.description.abstractThe biological processes that occur within a cell are known to be organized in networks. There are many types of biological networks, each of which perform a specific function and are known to interact. However, as the network structures themselves are complex, the nature and structure of the interactions across networks are difficult to define. In this paper we assume that if a set of networks interact, the dynamics within each of these networks must share a common latent signal. To identify this signal across multiple networks we propose a Gaussian Process Latent Factor Model (GP-LFM). Our proposed model uses a GP-LFM to represent the observed time course at each network node based on the assumption that a diffusion process governs the dynamics within each network. From the assumption that diffusion of a common latent factor generates all observed node time courses we derive a latent force model. This latent force model explicitly predicts each node's time course based only on the neighborhood of that node and the strength of the diffusion process within that node's network. We then extend this idea to multiple networks by assuming that each network can be considered independent once the common latent function which generates all node time courses over all networks is known. Finally, we consider the transductive case, where some node time courses in some networks are unobserved and we wish to infer them. We show that by a re-parameterization of our proposed GP-LFM model we can estimate these missing node time courses without deviating from the standard GP-LFM optimization methods. We evaluate the performance of our GP-LFM on simulated and real data and clearly show that our GP-LFM approach is capable of identifying a common latent signal which is determines known dynamics across multiple networks.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisher京都大学化学研究所ja
dc.publisher.alternativeInstitute for Chemical Research, Kyoto Universityen
dc.subject.ndc007-
dc.titleDatamining Biological Networksen
dc.title.alternative生物ネットワークのデータマイニングja
dc.typearticle-
dc.type.niitypeArticle-
dc.identifier.jtitle京都大学化学研究所スーパーコンピュータシステム研究成果報告書ja
dc.identifier.volume2012-
dc.identifier.spage75-
dc.identifier.epage77-
dc.textversionpublisher-
dc.sortkey34-
dc.address京都大学化学研究所 バイオインフォマティクスセンターen
dcterms.accessRightsopen access-
Appears in Collections:平成24年度

Show simple item record

Export to RefWorks


Export Format: 


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.