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Microarray Image Analysis Using Genetic Algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sivalakshmi, B. Rao, N. Naga Malleswara |
| Copyright Year | 2017 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iaescore.com/journals/index.php/IJEECS/article/download/6001/5376 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Cluster analysis Ferric Carboxymaltose Solution Fosfomycin Fuzzy cognitive map Gene expression profiling Gene regulatory network Genetic algorithm Image analysis Information extraction International Standard Serial Number K-means clustering Microarray Analysis Optimizing compiler Segmentation action Transcription (software) biologic segmentation statistical cluster |
| Content Type | Text |
| Resource Type | Article |
