3 research outputs found
Multiconstrained gene clustering based on generalized projections
<p>Abstract</p> <p>Background</p> <p>Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem.</p> <p>Results</p> <p>We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods.</p> <p>Conclusions</p> <p>The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions.</p
Learning Topic Models by Belief Propagation
Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model
for probabilistic topic modeling, which attracts worldwide interests and
touches on many important applications in text mining, computer vision and
computational biology. This paper represents LDA as a factor graph within the
Markov random field (MRF) framework, which enables the classic loopy belief
propagation (BP) algorithm for approximate inference and parameter estimation.
Although two commonly-used approximate inference methods, such as variational
Bayes (VB) and collapsed Gibbs sampling (GS), have gained great successes in
learning LDA, the proposed BP is competitive in both speed and accuracy as
validated by encouraging experimental results on four large-scale document data
sets. Furthermore, the BP algorithm has the potential to become a generic
learning scheme for variants of LDA-based topic models. To this end, we show
how to learn two typical variants of LDA-based topic models, such as
author-topic models (ATM) and relational topic models (RTM), using BP based on
the factor graph representation.Comment: 14 pages, 17 figure