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https://doi.org/10.1007/978-3-642-03845-7_17
DC Field | Value | |
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dc.title | Probabilistic approximations of signaling pathway dynamics | |
dc.contributor.author | Liu, B. | |
dc.contributor.author | Thiagarajan, P.S. | |
dc.contributor.author | Hsu, D. | |
dc.date.accessioned | 2013-07-04T08:02:17Z | |
dc.date.available | 2013-07-04T08:02:17Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Liu, B.,Thiagarajan, P.S.,Hsu, D. (2009). Probabilistic approximations of signaling pathway dynamics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5688 LNBI : 251-265. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-03845-7_17" target="_blank">https://doi.org/10.1007/978-3-642-03845-7_17</a> | |
dc.identifier.isbn | 3642038441 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/40349 | |
dc.description.abstract | Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic approximation of the ODE dynamics by discretizing the value space and the time domain. We then sample a representative set of trajectories and exploit the discretization and the structure of the signaling pathway to encode these trajectories compactly as a dynamic Bayesian network. As a result, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques. We have tested our method on a model of EGF-NGF signaling pathway [1] and the results are very promising in terms of both accuracy and efficiency. © 2009 Springer Berlin Heidelberg. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-03845-7_17 | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.doi | 10.1007/978-3-642-03845-7_17 | |
dc.description.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.description.volume | 5688 LNBI | |
dc.description.page | 251-265 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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