Please use this identifier to cite or link to this item:
|Title:||Composing globally consistent pathway parameter estimates through belief propagation|
|Citation:||Koh, G.,Tucker-Kellogg, L.,Hsu, D.,Thiagarajan, P.S. (2007). Composing globally consistent pathway parameter estimates through belief propagation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4645 LNBI : 420-430. ScholarBank@NUS Repository.|
|Abstract:||Parameter estimation of large bio-pathway models is an important and difficult problem. To reduce the prohibitive computational cost, one approach is to decompose a large model into components and estimate their parameters separately. However, the decomposed components often share common parts that may have conflicting parameter estimates, as they are computed independently within each component. In this paper, we propose to use a probabilistic inference technique called belief propagation to reconcile these independent estimates in a principled manner and compute new estimates that are globally consistent and fit well with data. An important advantage of our approach in practice is that it naturally handles incomplete or noisy data. Preliminary results based on synthetic data show promising performance in terms of both accuracy and efficiency. © Springer-Verlag Berlin Heidelberg 2007.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 3, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.