Heterogeneous CPU+iGPU Processing for Efficient Epistasis Detection

Author:

Campos Rafael,Marques Diogo,Santander-Jiménez Sergio,Sousa Leonel,Ilic Aleksandar

Publisher

Springer International Publishing

Reference19 articles.

1. Che, K., et al.: Epistasis detection using a permutation-based gradient boosting machine. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1247–1252. IEEE (2016)

2. Dinu, I., et al.: SNP-SNP interactions discovered by logic regression explain Crohn’s disease genetics. PloS One 7(10), e43035 (2012)

3. Fan, R., et al.: Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases. Genet. Epidemiol. 35(7), 706–721 (2011)

4. Lecture Notes in Computer Science;D Gallego-Sánchez,2017

5. Gonçalves, F., et al.: Parallel evolutionary computation for multiobjective gene interaction analysis. J. Comput. Sci. 40(101068), 1–15 (2020)

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1. Interpreting High Order Epistasis Using Sparse Transformers;Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies;2023-06-21

2. Tensor-Accelerated Fourth-Order Epistasis Detection on GPUs;Proceedings of the 51st International Conference on Parallel Processing;2022-08-29

3. A SIMD algorithm for the detection of epistatic interactions of any order;Future Generation Computer Systems;2022-07

4. Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study;2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2022-05

5. Fiuncho: a program for any-order epistasis detection in CPU clusters;The Journal of Supercomputing;2022-04-16

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