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Optimisation of HMM Topologies Enhances DNA and Protein Sequence Modelling
-
Torben Friedrich
Veröffentlicht/Copyright:
6. Januar 2010
Hidden Markov models (HMMs) play a major role in applications to unravel biomolecular functionality. Though HMMs are technically mature and widely applied in computational biology, there is a potential of methodical optimisation concerning its modelling of biological data sources with varying sequence lengths.Single building blocks of these models, the states, are associated with a certain holding time, being the link to the length distribution of represented sequence motifs. An adaptation of regular HMM topologies to bell-shaped sequence lengths is achieved by a serial chain-linking of hidden states, while residing in the class of conventional hidden Markov models. The factor of the repetition of states (r) and the parameter for state-specific duration of stay (p) are determined by fitting the distribution of sequence lengths with the method of moments (MM) and maximum likelihood (ML). Performance evaluations of differently adjusted HMM topologies underline the impact of an optimisation for HMMs based on sequence lengths. Secondary structure prediction on internal transcribed spacer 2 sequences demonstrates exemplarily the general impact of topological optimisations. In summary, we propose a general methodology to improve the modelling behaviour of HMMs by topological optimisation with ML and a fast and easily implementable moment estimator.
Keywords: artificial intelligence; biostatistics; hidden Markov models; mathematical modelling; pattern recognition; statistics
Published Online: 2010-1-6
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Schlagwörter für diesen Artikel
artificial intelligence;
biostatistics;
hidden Markov models;
mathematical modelling;
pattern recognition;
statistics
Artikel in diesem Heft
- Article
- Epistatic Interactions
- Testing for Gene-Gene Interaction with AMMI Models
- A Bayesian Hierarchical Model for Quantitative Real-Time PCR Data
- Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach
- Detecting Genotyping Error Using Measures of Degree of Hardy-Weinberg Disequilibrium
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- The Apportionment of Total Genetic Variation by Categorical Analysis of Variance
- Dealing with Heterogeneity between Cohorts in Genomewide SNP Association Studies
- An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data
- Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment
- Asymptotic Distribution of the "Orthogonal" Quantitative Transmission Disequilibrium Test in a Structured Population: Exact Formula
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- Trilocus Disequilibrium Analysis of Multiallelic Markers in Outcrossing Populations
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- Sub-Modular Resolution Analysis by Network Mixture Models
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- Network Enrichment Analysis in Complex Experiments
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- Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data
- A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number
- Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression
- Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA
- Confidently Estimating the Number of DNA Replication Origins
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- Granger Causality Analysis of Human Cell-Cycle Gene Expression Profiles
- Mapping Quantitative Trait Loci in a Non-Equilibrium Population
- On the Optimal Design of Genetic Variant Discovery Studies
- On Optimal Selection of Summary Statistics for Approximate Bayesian Computation
- Assessment of LD Matrix Measures for the Analysis of Biological Pathway Association
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