Lossless Compression of DNA Microarray Images

Microarray experiments are characterized by a massive amount of data, usually in the form of an image. Based on the nature of microarray images, we consider the microarray in terms of its structure and statistics. Based on the microarray image model, we propose a context-based method for lossless compression of microarray images using prediction by partial approximate matching (PPAM). In synchronization experiments, the raw data consists of two channel microarray images. The correlation between these two channel microarray images is explored in order to improve the compression performance. Our results show that, the proposed approach produces a better compression result when compared with results from the best-known microarray compression algorithm.

• Type: Conference
• Year: 2005
• Where: CSB
• Authors: Yong Zhang, Rahul Parthe, Donald A. Adjeroh
• Keywords: Bioinformatics, Channel Microarray Images, CSB 2005, Microarray Image Model, Microarray Images