Literature DB >> 18541487

An original genetic approach to the fully automatic gridding of microarray images.

Eleni Zacharia1, Dimitris Maroulis.   

Abstract

Gridding microarray images remains, at present, a major bottleneck. It requires human intervention which causes variations of the gene expression results. In this paper, an original and fully automatic approach for accurately locating a distorted grid structure in a microarray image is presented. The gridding process is expressed as an optimization problem which is solved by using a genetic algorithm (GA). The GA determines the line-segments constituting the grid structure. The proposed method has been compared with existing software tools as well as with a recently published technique. For this purpose, several real and artificial microarray images containing more than one million spots have been used. The outcome has shown that the accuracy of the proposed method achieves the high value of 94% and it outperforms the existing approaches. It is also noise-resistant and yields excellent results even under adverse conditions such as arbitrary grid rotations, and the appearance of various spot sizes.

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Year:  2008        PMID: 18541487     DOI: 10.1109/TMI.2008.915561

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  4 in total

1.  Low-complexity PDE-based approach for automatic microarray image processing.

Authors:  Bogdan Belean; Romulus Terebes; Adrian Bot
Journal:  Med Biol Eng Comput       Date:  2014-10-29       Impact factor: 2.602

2.  A glance at DNA microarray technology and applications.

Authors:  Amir Ata Saei; Yadollah Omidi
Journal:  Bioimpacts       Date:  2011-08-04

3.  Automatic Spot Identification for High Throughput Microarray Analysis.

Authors:  Eunice Wu; Yan A Su; Eric Billings; Bernard R Brooks; Xiongwu Wu
Journal:  J Bioeng Biomed Sci       Date:  2011-11-18

4.  M3G: maximum margin microarray gridding.

Authors:  Dimitris Bariamis; Dimitris K Iakovidis; Dimitris Maroulis
Journal:  BMC Bioinformatics       Date:  2010-01-25       Impact factor: 3.169

  4 in total

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