Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 80163 Pages 1-13 DOI ASP 2006 80163 An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches Peter Bajcsy The National Center for Supercomputing Applications University of Illinois at Urbana-Champaign IL 61801 USA Received 1 May 2005 Revised 11 October 2005 Accepted 15 December 2005 This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information and hence impact our confidence in any data-derived biological conclusions. Thus understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION The discovery of microarray technology in 1995 has opened new avenues for investigating gene expressions 1 and introduced new information problems 2 3 . Researchers have developed several microarray data processing methods and modeling techniques that are specific to DNA microarray analysis 4 and with the objective to draw biologically meaningful conclusions 5-8 . However the analysis of DNA microarray data consists of several processing steps 9 that can significantly deteriorate the quality of gene expression information and hence lower our confidence in any derived research result. Thus understanding microarray data processing steps 10 becomes critical for performing optimal .
