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A new hardware architecture for performing the gridding of DNA microarray images
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Source Great Lakes Symposium on VLSI archive
Proceedings of the 17th ACM Great Lakes symposium on VLSI table of contents
Stresa-Lago Maggiore, Italy
SESSION: ASIP/ASIC table of contents
Pages: 341 - 346  
Year of Publication: 2007
ISBN:978-1-59593-605-9
Authors
Luca Sterpone  Politecnico di Torino, Torino, Italy
Massimo Violante  Politecnico di Torino, Torino, Italy
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

DNA microarray technologies are an essential part of modern biomedical research. The analysis of DNA microarray images allows the identification of gene expressions in order to drawn biologically meaningful conclusions for applications that ranges from the genetic profiling to the diagnosis of oncology diseases. Unfortunately, DNA microarray technology has a high variation of data quality. Therefore, in order to obtain reliable results, complex and extensive image analysis algorithms should be applied before actual DNA microarray information can be used for biomedical purpose. In this paper, we present a novel hardware acceleration architecture specifically designed to process DNA microarray images. The proposed architecture uses several units working in a single instruction-multiple data fashion managed by a microprocessor core. An FPGA-based prototypal implementation of the developed architecture is presented. Experimental results on several realistic DNA microarray images show a reduction of the computation time of one order of magnitude if compared with previously developed software-based approach.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Amos Mosseri and Eitan Hirsh, "Analysis of Gene Expression Data," Lecture 3, Tel Aviv University, 2005.
 
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Standford University, "Stanford Microarray Database," Available: http://smd.stanford.edu/
 
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Collaborative Colleagues:
Luca Sterpone: colleagues
Massimo Violante: colleagues