Journal of clinical monitoring and computing
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Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.
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J Clin Monit Comput · Oct 2005
Randomized and parallel algorithms for distance matrix calculations in multiple sequence alignment.
Multiple sequence alignment (MSA) is a vital problem in biology. Optimal alignment of multiple sequences becomes impractical even for a modest number of sequences since the general version of the problem is NP-hard. Because of the high time complexity of traditional MSA algorithms, even today's fast computers are not able to solve the problem for large number of sequences. ⋯ We show that our algorithms are amenable to parallelism in Section. In Section we back up our claim of speedup and accuracy with empirical data and examples. In Section we provide some concluding remarks.
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J Clin Monit Comput · Oct 2005
Reverse-engineering gene-regulatory networks using evolutionary algorithms and grid computing.
Living organisms regulate the expression of genes using complex interactions of transcription factors, messenger RNA and active protein products. Due to their complexity, gene-regulatory networks are not fully understood.However, by building computational models it is possible to gain insight into their function and operation. ⋯ Determining network models of gene-regulatory networks using evolutionary algorithms not only requires considerable computational power, but also a modeling formalism that can explain the underlying dynamics.
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The human genome project has resulted in the generation of voluminous biological data. Novel computational techniques are called for to extract useful information from this data. One such technique is that of finding patterns that are repeated over many sequences (and possibly over many species). In this paper we study the problem of identifying meaningful patterns (i.e., motifs) from biological data, the motif search problem. ⋯ All the algorithms proposed in this paper are improvements over existing algorithms for these versions of motif search in biological sequence data. The algorithms presented have the potential of performing well in practice.