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Combining GPU and FPGA technology for efficient exhaustive interaction analysis in GWAS.

, , , , and . ASAP, page 170-175. IEEE Computer Society, (2016)

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Genome-Wide Association Interaction Studies with MB-MDR and maxT multiple testing correction on FPGAs., , and . ICCS, volume 80 of Procedia Computer Science, page 639-649. Elsevier, (2016)Hybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS., , , and . Euro-Par, volume 8632 of Lecture Notes in Computer Science, page 680-691. Springer, (2014)Large-scale genome-wide association studies on a GPU cluster using a CUDA-accelerated PGAS programming model., , , and . Int. J. High Perform. Comput. Appl., 29 (4): 506-510 (2015)UPC++ for bioinformatics: A case study using genome-wide association studies., , , and . CLUSTER, page 248-256. IEEE Computer Society, (2014)The FPGA-Based High-Performance Computer RIVYERA for Applications in Bioinformatics.. CiE, volume 8493 of Lecture Notes in Computer Science, page 383-392. Springer, (2014)Network Aggregation to Enhance Results Derived from Multiple Analytics., , , and . AIAI (1), volume 583 of IFIP Advances in Information and Communication Technology, page 128-140. Springer, (2020)1, 000x Faster Than PLINK: Genome-Wide Epistasis Detection with Logistic Regression Using Combined FPGA and GPU Accelerators., , , and . ICCS (2), volume 10861 of Lecture Notes in Computer Science, page 368-381. Springer, (2018)EagleImp: fast and accurate genome-wide phasing and imputation in a single tool., and . Bioinform., 38 (22): 4999-5006 (November 2022)FPGAs in Bioinformatics - Implementation and Evaluation of Common Bioinformatics Algorithms in Reconfigurable Logic.. University of Kiel, (2016)1000× faster than PLINK: Combined FPGA and GPU accelerators for logistic regression-based detection of epistasis., , , and . J. Comput. Sci., (2019)