3,480 research outputs found
Applications of Hidden Markov Models in Microarray Gene Expression Data
Hidden Markov models (HMMs) are well developed statistical models to capture hidden information from observable sequential symbols. They were first used in speech recognition in 1970s and have been successfully applied to the analysis of biological sequences since late 1980s as in finding protein secondary structure, CpG islands and families of related DNA or protein sequences [1]. In a HMM, the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. In this chapter, we described two applications using HMMs to predict gene functions in yeast and DNA copy number alternations in human tumor cells, based on gene expression microarray data
Improved functional prediction of proteins by learning kernel combinations in multilabel settings
Background
We develop a probabilistic model for combining kernel matrices to predict the function of proteins. It extends previous approaches in that it can handle multiple labels which naturally appear in the context of protein function.
Results
Explicit modeling of multilabels significantly improves the capability of learning protein function from multiple kernels. The performance and the interpretability of the inference model are further improved by simultaneously predicting the subcellular localization of proteins and by combining pairwise classifiers to consistent class membership estimates.
Conclusion
For the purpose of functional prediction of proteins, multilabels provide valuable information that should be included adequately in the training process of classifiers. Learning of functional categories gains from co-prediction of subcellular localization. Pairwise separation rules allow very detailed insights into the relevance of different measurements like sequence, structure, interaction data, or expression data. A preliminary version of the software can be downloaded from http://www.inf.ethz.ch/personal/vroth/KernelHMM/.ISSN:1471-210
Bayesian machine learning methods for predicting protein-peptide interactions and detecting mosaic structures in DNA sequences alignments
Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes
and biochemical pathways. High-throughput experiments like yeast two-hybrid and phage
display are expensive and intrinsically noisy, therefore it would be desirable to target informative interactions and pursue in silico approaches. We propose a probabilistic discriminative
approach for predicting PRM-mediated protein-protein interactions from sequence data. The
model suffered from over-fitting, so Laplacian regularisation was found to be important in
achieving a reasonable generalisation performance. A hybrid approach yielded the best performance, where the binding site motifs were initialised with the predictions of a generative
model. We also propose another discriminative model which can be applied to all sequences
present in the organism at a significantly lower computational cost. This is due to its additional
assumption that the underlying binding sites tend to be similar.It is difficult to distinguish between the binding site motifs of the PRM due to the small
number of instances of each binding site motif. However, closely related species are expected
to share similar binding sites, which would be expected to be highly conserved. We investigated
rate variation along DNA sequence alignments, modelling confounding effects such as recombination. Traditional approaches to phylogenetic inference assume that a single phylogenetic
tree can represent the relationships and divergences between the taxa. However, taxa sequences
exhibit varying levels of conservation, e.g. due to regulatory elements and active binding sites,
and certain bacteria and viruses undergo interspecific recombination. We propose a phylogenetic factorial hidden Markov model to infer recombination and rate variation. We examined
the performance of our model and inference scheme on various synthetic alignments, and compared it to state of the art breakpoint models. We investigated three DNA sequence alignments:
one of maize actin genes, one bacterial (Neisseria), and the other of HIV-1. Inference is carried
out in the Bayesian framework, using Reversible Jump Markov Chain Monte Carlo
Gene3D: comprehensive structural and functional annotation of genomes
Gene3D provides comprehensive structural and functional annotation of most available protein sequences, including the UniProt, RefSeq and Integr8 resources. The main structural annotation is generated through scanning these sequences against the CATH structural domain database profile-HMM library. CATH is a database of manually derived PDB-based structural domains, placed within a hierarchy reflecting topology, homology and conservation and is able to infer more ancient and divergent homology relationships than sequence-based approaches. This data is supplemented with Pfam-A, other non-domain structural predictions (i.e. coiled coils) and experimental data from UniProt. In order to enhance the investigations possible with this data, we have also incorporated a variety of protein annotation resources, including protein–protein interaction data, GO functional assignments, KEGG pathways, FUNCAT functional descriptions and links to microarray expression data. All of this data can be accessed through a newly re-designed website that has a focus on flexibility and clarity, with searches that can be restricted to a single genome or across the entire sequence database. Currently Gene3D contains over 3.5 million domain assignments for nearly 5 million proteins including 527 completed genomes. This is available at: http://gene3d.biochem.ucl.ac.uk
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