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Accelerating Processing Speed in Pathway Research Based on GPU

机译:基于GPU的途径研究加速加工速度

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Genome-wide association study (GWAS) has become an effective and successful method to identify disease loci by considering SNPs independently. However, it may be invalid for uncovering the disease loci that not reaching a stringent genome-wide significance threshold. As a result, multi-SNP GWAS is developing rapidly as a complement to traditional GWAS. However, the high computational cost becomes a major limitation for it. The graphical processing unit (GPU) is a programmable graphics processor which has powerful parallel computing ability. And with the development, GPUs have been feasible for many scientific studies. Hence, we are motivated to use GPUs for pathway-based GWAS to improve computational efficiency. The experiment results attained showed the speed-up ratio can reach up to more than 160.
机译:基因组 - 范围协会研究(GWAs)已成为通过独立考虑SNP来识别疾病基因座的有效和成功的方法。然而,对于未达到严格的基因组显着性阈值的疾病基因座可能是无效的。结果,多SNP GWA正在迅速发展,作为传统GWA的补充。然而,高计算成本成为它的主要限制。图形处理单元(GPU)是一种可编程图形处理器,具有强大的并行计算能力。随着发展,GPU对于许多科学研究来说都是可行的。因此,我们有动力使用GPU用于基于途径的GWA来提高计算效率。获得的实验结果显示出速度比率可达超过160。

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