By Nora Speer, Christian Spieth, Andreas Zell (auth.), Rita Casadio, Gene Myers (eds.)
This booklet constitutes the refereed complaints of the fifth overseas Workshop on Algorithms in Bioinformatics, WABI 2005, held in Mallorca, Spain, in September 2005 as a part of the ALGO 2005 convention meetings.
The 34 revised complete papers offered have been rigorously reviewed and chosen from ninety five submissions. All present problems with algorithms in bioinformatics are addressed with designated specialise in statistical and probabilistic algorithms within the box of molecular and structural biology. The papers are equipped in topical sections on expression (hybrid tools and time patterns), phylogeny (quartets, tree reconciliation, clades and haplotypes), networks, genome rearrangements (transposition version and different models), sequences (strings, multi-alignment and clustering, clustering and representation), and constitution (threading and folding).
Read or Download Algorithms in Bioinformatics: 5th International Workshop, WABI 2005, Mallorca, Spain, October 3-6, 2005. Proceedings PDF
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Extra resources for Algorithms in Bioinformatics: 5th International Workshop, WABI 2005, Mallorca, Spain, October 3-6, 2005. Proceedings
The general assumption behind HPMs is that the genes of a given component pass through phases or biological states over the time. This means that, for a given component, we assume that some ranges of consecutive times actually correspond to the same biological state. These phases are hidden, but they aﬀect the mean expression level evolution of the component. For example, some phases induce an increase in the mean level expression level while others tend to decrease or stabilize the level. In the same manner, the increase (or decrease) can be high for some phases and low for others, etc.
P. C. G. Weinstein, A. Abdelhakim, S. W. B. P. Bartel. The microRNAs of C. elegans. Genes and Development, 17:991–1008, 2003. 19. C. Llave, Z. D. C. Carrington. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science, 297:2053–2056, 2002. 20. H. Mathews, J. Sabina, M. H. Turner. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. , 288:911–940, 1999. 21. N. C. Socci. Computational identification of microRNA targets.
A mixture model is used to describe and classify the data. The parameters of this model are constrained by a prior distribution deﬁned with a new type of model that can express both our prior knowledge about the proﬁle of classes of interest and the temporal nature of the data. Then, an EM algorithm estimates the parameters of the mixture model by maximizing its posterior probability. pdf 1 Introduction Technological advances such as microarrays allow us to simultaneously measure the level of expression of thousands of genes in a given tissue over time —for example along the cell cycle .