论文信息 - FPGA-accelerated Bayesian learning for reconstruction of gene regulatory networks

FPGA-accelerated Bayesian learning for reconstruction of gene regulatory networks

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.

[1] David Maxwell Chickering,et al. Learning Bayesian Networks is , 1994 .

[2] Dirk Husmeier,et al. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[3] Lorenz Wernisch,et al. Reconstruction of gene networks using Bayesian learning and manipulation experiments , 2004, Bioinform..

[4] Nir Friedman,et al. From promoter sequence to expression: a probabilistic framework , 2002, RECOMB '02.

[5] Michal Linial,et al. Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[6] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[7] Martin C. Herbordt,et al. Case study of a functional genomics application for an FPGA-based coprocessor , 2004, Microprocess. Microsystems.

[8] I Pournara,et al. Reconstructing gene networks by passive and active Bayesian learning. , 2005 .

[9] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[10] Tommi S. Jaakkola,et al. Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks , 2000, Pacific Symposium on Biocomputing.