Towards parallel largescale genomic prediction by coupling sparse and dense matrix algebra
 Author
 Arne De Coninck (UGent) , Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Bernard De Baets (UGent) , Steven Maenhout (UGent) and Jan Fostier (UGent)
 Organization
 Project
 Abstract
 Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with largescale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra.
 Keywords
 biology computing, data analysis, parallel processing, dense matrix algebra, genomics, sparse matrices, coupling sparse, environmental condition, sparse matrix algebra, high performance computing infrastructure, Bioinformatics, sparse matrix formalism, Genomics, parallel largescale genomic prediction, Computational modeling, Sparse matrices, Mathematical model, Predictive models, plant breeding, sparse matrix algebra, distributed computing, data analysis, genomic prediction
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Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU6934376
 MLA
 De Coninck, Arne, et al. “Towards Parallel LargeScale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra.” 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015), edited by Masoud Daneshtalab et al., IEEE, 2015, pp. 747–50.
 APA
 De Coninck, A., Kourounis, D., Verbosio, F., Schenk, O., De Baets, B., Maenhout, S., & Fostier, J. (2015). Towards parallel largescale genomic prediction by coupling sparse and dense matrix algebra. In M. Daneshtalab, M. Aldinucci, V. Leppänen, J. Lilius, & M. Brorsson (Eds.), 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015) (pp. 747–750). New York, NY, USA: IEEE.
 Chicago authordate
 De Coninck, Arne, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Bernard De Baets, Steven Maenhout, and Jan Fostier. 2015. “Towards Parallel LargeScale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra.” In 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015), edited by Masoud Daneshtalab, Marco Aldinucci, Ville Leppänen, Johan Lilius, and Mats Brorsson, 747–50. New York, NY, USA: IEEE.
 Chicago authordate (all authors)
 De Coninck, Arne, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Bernard De Baets, Steven Maenhout, and Jan Fostier. 2015. “Towards Parallel LargeScale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra.” In 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015), ed by. Masoud Daneshtalab, Marco Aldinucci, Ville Leppänen, Johan Lilius, and Mats Brorsson, 747–750. New York, NY, USA: IEEE.
 Vancouver
 1.De Coninck A, Kourounis D, Verbosio F, Schenk O, De Baets B, Maenhout S, et al. Towards parallel largescale genomic prediction by coupling sparse and dense matrix algebra. In: Daneshtalab M, Aldinucci M, Leppänen V, Lilius J, Brorsson M, editors. 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015). New York, NY, USA: IEEE; 2015. p. 747–50.
 IEEE
 [1]A. De Coninck et al., “Towards parallel largescale genomic prediction by coupling sparse and dense matrix algebra,” in 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015), Turku, Finland, 2015, pp. 747–750.
@inproceedings{6934376, abstract = {Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with largescale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra.}, author = {De Coninck, Arne and Kourounis, Drosos and Verbosio, Fabio and Schenk, Olaf and De Baets, Bernard and Maenhout, Steven and Fostier, Jan}, booktitle = {23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORKBASED PROCESSING (PDP 2015)}, editor = {Daneshtalab, Masoud and Aldinucci, Marco and Leppänen, Ville and Lilius, Johan and Brorsson, Mats}, isbn = {9781479984909}, keywords = {biology computing,data analysis,parallel processing,dense matrix algebra,genomics,sparse matrices,coupling sparse,environmental condition,sparse matrix algebra,high performance computing infrastructure,Bioinformatics,sparse matrix formalism,Genomics,parallel largescale genomic prediction,Computational modeling,Sparse matrices,Mathematical model,Predictive models,plant breeding,sparse matrix algebra,distributed computing,data analysis,genomic prediction}, language = {eng}, location = {Turku, Finland}, pages = {747750}, publisher = {IEEE}, title = {Towards parallel largescale genomic prediction by coupling sparse and dense matrix algebra}, url = {http://dx.doi.org/10.1109/PDP.2015.94}, year = {2015}, }
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