2002 AHierarchicalBayesianMarkovianMod

• (Xing et al., 2002a) ⇒ Eric P. Xing, Michael I. Jordan, Richard M. Karp, and Stuart Russell. (2002). “A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences.” In: Proceedings of Advances in Neural Information Processing Systems (NIPS 2002).

Subject Headings: Dynamic Bayesian Model.

Quotes

Abstract

We propose a dynamic Bayesian model for motifs in biopolymer sequences which captures rich biological prior knowledge and positional dependencies in motif structure in a principled way. Our model posits that the position-specific multinomial parameters for monomer distribution are distributed as a latent Dirichlet-mixture random variable, and the position-specific Dirichlet component is determined by a hidden Markov process. Model parameters can be fit on training motifs using a variational EM algorithm within an empirical Bayesian framework. Variational inference is also used for detecting hidden motifs. Our model improves over previous models that ignore biological priors and positional dependence. It has much higher sensitivity to motifs during detection and a notable ability to distinguish genuine motifs from false recurring patterns.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 AHierarchicalBayesianMarkovianMod	Eric P. Xing Richard M. Karp Stuart J. Russell Michael I. Jordan			A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences		Proceedings of Advances in Neural Information Processing System	http://www.cs.berkeley.edu/~russell/papers/nips02-motif.pdf			2002