Microarray Image Analysis Using Genetic Algorithm
Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation an...
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| Main Authors: | , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2016-12-01.
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| Online Access: | Get fulltext |
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| LEADER | 02254 am a22002653u 4500 | ||
|---|---|---|---|
| 001 | ijeecs6001_5376 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Sivalakshmi, Bolem |e author |
| 700 | 1 | 0 | |a Malleswara Rao, N. Naga |e author |
| 245 | 0 | 0 | |a Microarray Image Analysis Using Genetic Algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2016-12-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6001 | ||
| 520 | |a Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. This paper presents microarray image analysis using Genetic Algorithm. A new algorithm for microarray image contrast enhancement is presented using Genetic Algorithm. Contrast enhancement is crucial step in extracting edge information in image and finally this edge information is used in gridding of microarray image. Mostly segmentation of microarray image is carried out using clustering algorithms. Clustering algorithms have an advantage that they are not restricted to a particular shape and size for the spots. In this paper, segmentation using Genetic Algorithm by optimizing K-means index and Jm measure is presented. The qualitative analysis shows that the proposed method achieves better segmentation results than K-means and FCM algorithms. | ||
| 540 | |a Copyright (c) 2016 Indonesian Journal of Electrical Engineering and Computer Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 4, No 3: December 2016; 561-567 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v4.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6001/5376 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6001/5376 |z Get fulltext |