Results 11  20
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415
Combining phylogenetic and hidden Markov models in biosequence analysis
 J. Comput. Biol
, 2004
"... A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individ ..."
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Cited by 103 (12 self)
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A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction—for gene finding, for example, or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higherorder states in the HMM, which allows for contextsensitive models of substitution— that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higherorder states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higherorder states being particularly pronounced.
PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny
 PLoS Comput Biol 2005
"... A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This anal ..."
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Cited by 83 (5 self)
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A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and ‘‘background’ ’ intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markovchain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motiffinding
Probabilistic and Statistical Properties of Words: An Overview
 Journal of Computational Biology
, 2000
"... In the following, an overview is given on statistical and probabilistic properties of words, as occurring in the analysis of biological sequences. Counts of occurrence, counts of clumps, and renewal counts are distinguished, and exact distributions as well as normal approximations, Poisson process a ..."
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Cited by 83 (1 self)
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In the following, an overview is given on statistical and probabilistic properties of words, as occurring in the analysis of biological sequences. Counts of occurrence, counts of clumps, and renewal counts are distinguished, and exact distributions as well as normal approximations, Poisson process approximations, and compound Poisson approximations are derived. Here, a sequence is modelled as a stationary ergodic Markov chain; a test for determining the appropriate order of the Markov chain is described. The convergence results take the error made by estimating the Markovian transition probabilities into account. The main tools involved are moment generating functions, martingales, Stein’s method, and the ChenStein method. Similar results are given for occurrences of multiple patterns, and, as an example, the problem of unique recoverability of a sequence from SBH chip data is discussed. Special emphasis lies on disentangling the complicated dependence structure between word occurrences, due to selfoverlap as well as due to overlap between words. The results can be used to derive approximate, and conservative, con � dence intervals for tests. Key words: word counts, renewal counts, Markov model, exact distribution, normal approximation, Poisson process approximation, compound Poisson approximation, occurrences of multiple words, sequencing by hybridization, martingales, moment generating functions, Stein’s method, ChenStein method. 1.
Sentence Fusion for Multidocument News Summarization
 Lexical cohesion, the thesaurus, and the structure of text. Computational Linguistics, 17(1):21–48. Nenkova, Ani
, 1991
"... A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel texttotext generation technique for synthesi ..."
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Cited by 77 (4 self)
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A system that can produce informative summaries, highlighting common information found in many online documents, will help Web users to pinpoint information that they need without extensive reading. In this article, we introduce sentence fusion, a novel texttotext generation technique for synthesizing common information across documents. Sentence fusion involves bottomup local multisequence alignment to identify phrases conveying similar information and statistical generation to combine common phrases into a sentence. Sentence fusion moves the summarization field from the use of purely extractive methods to the generation of abstracts that contain sentences not found in any of the input documents and can synthesize information across sources. 1.
Prediction of signal peptides and signal anchors by a hidden Markov model
 Proc. Int. Conf. Intell. Syst. Mol. Biol
, 1998
"... A hidden Markov model of signal peptides has been developed. It contains submodels for the Nterminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our dat ..."
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Cited by 77 (8 self)
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A hidden Markov model of signal peptides has been developed. It contains submodels for the Nterminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Grampositive bacteria, and Gramnegative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70 % of signal peptides in a crossvalidated test—almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.
Phylogenetic Tree Construction Using Markov Chain Monte Carlo
, 1999
"... We describe a Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences. Under simple models of mutational events, our method produces a Markov chain whose stationary distribution is the conditional distribution of the phylogeny given the obs ..."
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Cited by 57 (0 self)
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We describe a Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences. Under simple models of mutational events, our method produces a Markov chain whose stationary distribution is the conditional distribution of the phylogeny given the observed sequences. Our algorithm strikes a reasonable balance between the desire to move globally through the space of phylogenies and the need to make computationally feasible moves in areas of high probability. Since phylogenetic information is described by a tree, we have created new diagnostics to handle this type of data structure. An important byproduct of the Markov chain Monte Carlo phylogeny building technique is that it provides estimates and corresponding measures of variability for any aspect of the phylogeny under study.
Efficient Multiple Genome Alignment
, 2002
"... Motivation: To allow a direct comparison of the genomic DNA sequences of sufficiently similar organisms, there is an urgent need for software tools that can align more than two genomic sequences. Results: We developed... ..."
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Cited by 48 (9 self)
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Motivation: To allow a direct comparison of the genomic DNA sequences of sufficiently similar organisms, there is an urgent need for software tools that can align more than two genomic sequences. Results: We developed...
Structured prediction, dual extragradient and Bregman projections
 Journal of Machine Learning Research
, 2006
"... We present a simple and scalable algorithm for maximummargin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convexconcave saddlepoint problem that allows us to use simple projection methods ..."
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Cited by 45 (2 self)
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We present a simple and scalable algorithm for maximummargin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convexconcave saddlepoint problem that allows us to use simple projection methods based on the dual extragradient algorithm (Nesterov, 2003). The projection step can be solved using dynamic programming or combinatorial algorithms for mincost convex flow, depending on the structure of the problem. We show that this approach provides a memoryefficient alternative to formulations based on reductions to a quadratic program (QP). We analyze the convergence of the method and present experiments on two very different structured prediction tasks: 3D image segmentation and word alignment, illustrating the favorable scaling properties of our algorithm. 1 1.
Bootstrapping Lexical Choice via MultipleSequence Alignment
 IN PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP
, 2002
"... An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically, ..."
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Cited by 44 (4 self)
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An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically,