Analysis of Genetic Expression with Microarrays using GPU Implemented Algorithms
Centro de Investigaciones Biologicas del Noroeste S.C., Instituto Politecnico Nacional, La Paz, B.C.S., Mexico
Computacion y Sistemas, Vol. 17, No.3, pp. 357-364, 2013
@article{romero2013analysis,
title={Analysis of Genetic Expression with Microarrays using GPU Implemented Algorithms},
author={Romero-Vivas, Eduardo and Von Borstel, Fernando D and Villa-Medina, Isaac},
journal={Computaci{‘o}n y Sistemas},
volume={17},
number={3},
pages={357–364},
year={2013},
publisher={Instituto Polit{‘e}cnico Nacional}
}
DNA microarrays are used to simultaneously analyze the expression level of thousands of genes under multiple conditions; however, massive amount of data is generated making its analysis a challenge and an ideal candidate for massive parallel processing. Among the available technologies, the use of General Purpose computation on Graphics Processing Units (GPGPU) is an efficient cost-effective alternative, compared to a Central Processing Unit (CPU). This paper presents an implementation of algorithms using Compute Unified Device Architecture (CUDA) to determine statistical significance in the evaluation of gene expression levels for a microarray hybridization experiment designed and carried out at the Centro de Investigaciones Biologicas del Noroeste S.C. (CIBNOR). The obtained results are compared to traditional implementations.
October 24, 2013 by hgpu
Recent source codes
SYCL-BLAS: An implementation of BLAS using the SYCL open standard for acceleration on OpenCL devices
