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Using iterative MapReduce for parallel virtual screening
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- Ahmed, Laeeq (author)
- KTH,High Performance Computing and Visualization (HPCViz)
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- Edlund, Åke (author)
- KTH,High Performance Computing and Visualization (HPCViz)
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- Laure, Erwin (author)
- KTH,High Performance Computing and Visualization (HPCViz)
- show more...
- Spjuth, O. (author)
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- (creator_code:org_t)
- IEEE Computer Society, 2013
- 2013
- English.
- In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom). - : IEEE Computer Society. - 9780769550954 ; , s. 27-32
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- Virtual Screening is a technique in chemo informatics used for Drug discovery by searching large libraries of molecule structures. Virtual Screening often uses SVM, a supervised machine learning technique used for regression and classification analysis. Virtual screening using SVM not only involves huge datasets, but it is also compute expensive with a complexity that can grow at least up to O(n2). SVM based applications most commonly use MPI, which becomes complex and impractical with large datasets. As an alternative to MPI, MapReduce, and its different implementations, have been successfully used on commodity clusters for analysis of data for problems with very large datasets. Due to the large libraries of molecule structures in virtual screening, it becomes a good candidate for MapReduce. In this paper we present a MapReduce implementation of SVM based virtual screening, using Spark, an iterative MapReduce programming model. We show that our implementation has a good scaling behaviour and opens up the possibility of using huge public cloud infrastructures efficiently for virtual screening.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Big Data
- Chemoinformatics
- MapReduce
- Parallel SVM
- Spark
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- kon (subject category)
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