Case study of a functional genomics application for an FPGA-based coprocessor

Court, Tom Van; Herbordt, Martin C.; Barton, Richard J.

doi:10.1016/j.micpro.2004.03.005

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…This paper introduces a design that takes advantage of the reconfigurability of FPGAs and is easily parameterizable to networks with different number of nodes. Related work addressing the problem of linear regression on the gene expression microarray data using FPGAs can be found in [7]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

FPGA-accelerated Bayesian learning for construction of gene regulatory networks

Pournara

Bouganis

Constantinides

International Conference on Field Programmable Logic and Applications, 2005.

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Rapid advances in biological technologies, such as DNA microarrays, have enabled biologists to measure the expression levels of thousand of genes simultaneously under different conditions. This leads to a growing need to find methods that extract valuable information, fast and reliably, from this large amount of data. Recently, the advantages of using Bayesian networks for the reconstruction of gene regulatory networks from microarray data have been shown. However, these methods are very computationally intensive. Here, we explore the inherent parallelism of Bayesian learning and propose a hardware design that can be used for the reconstruction of such networks. The evaluation of the proposed design in a VirtexII demonstrates a speed up of the algorithm by 76 times over a software implementation in a Pentium 4.

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Section: Introductionmentioning

confidence: 99%