Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes.

Jun 27, 2000 - This program can export the final tree layout to BMP (bitmap. There are currently several to.

However, there are also many gene sets that do not preserve physical proximity. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models.

Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). Met - Art Fine Photography - Tinux there.

The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. Subtilis and E. Coli demonstrated that our model predicted many experimentally verified operons and functionally related clusters.

Our program is fast enough to provide a sevice on the web. Evolutionary algorithms and Genetic Programming (GP) in particular are increasingly being applied to the problem of evolving termweighting schemes in Information Retrieval (IR). One fundamental problem with the solutions generated by this stochastic, non-deterministic process is that they are often difficult to analyse. We develop a number of different distance measures between the phenotypes (ranked lists) of the solutions (term-weighting schemes) returned by a GP process. Using these distance measures, we develop trees which show how different solutions are clustered in the solution space. Using this framework we show that our evolved solutions lie in a different part of the solution space than two of the best benchmark term-weighting schemes available. The TreeJuxtaposer system [MGT ∗ 03] allowed visual comparison of large trees with guaranteed visibility for landmarks and Focus+Context navigation.

While that system allowed exploration and comparison of larger datasets than previous work, it was limited to a single tree of 775,000 nodes by a large memory footprint. With the current trend in the growing size of datasets we would like to be able to accommodate datasets of much larger trees. In this paper, we present theoretical limitations to TreeJuxtaposer’s architecture which severely restrict the ability to draw and interact with trees larger than what was previously attainable. We also provide two scalable, robust solutions to these limitations: TJC and TJC-Q. TJC is a system that supports browsing trees up to 15 million nodes by exploiting leading-edge graphics hardware while TJC-Q allows browsing trees up to 5 million node on commodity platforms. Both of these systems use a fast new algorithm for drawing and culling and benefit from a complete redesign of all data structures for more efficient memory usage and reduced preprocessing time.

Evolutionary algorithms and, in particular, Genetic Programming (GP) are increasingly being applied to the problem of evolving term-weighting schemes in Information Retrieval (IR). One fundamental problem with the solutions generated by these stochastic processes is that they are often difficult to analyse. Gdrc-86xt Remote Manual more. A number of questions regarding these evolved term-weighting schemes remain unanswered. One interesting question is; do different runs of the GP process bring us to similar points in the solution space? This paper deals with determining a number of measures of the distance between the ranked lists (phenotype) returned by different term-weighting schemes.