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: Evolutionary Techniques for Image Processing a Large Dataset of Early Drosophila Gene Expression | EURASIP Journal on Applied Signal Processing 2003 8 824-833 2003 Hindawi Publishing Corporation Evolutionary Techniques for Image Processing a Large Dataset of Early Drosophila Gene Expression Alexander Spirov Department of Applied Mathematics and Statistics and The Center for Developmental Genetics Stony Brook University Stony Brook NY 11794-3600 USA The Sechenov Institute of Evolutionary Physiology and Biochemistry Russian Academy of Sciences 44 Thorez Avenue St. Petersburg 194223 Russia Email spirov@ David M. Holloway Mathematics Department British Columbia Institute of Technology Burnaby British Columbia Canada V5G 3H2 Chemistry Department University of British Columbia Vancouver British Columbia Canada V6T1Z1 Email david_holloway@ Received 10 July 2002 and in revised form 1 December 2002 Understanding how genetic networks act in embryonic development requires a detailed and statistically significant dataset integrating diverse observational results. The fruit fly Drosophila melanogaster is used as a model organism for studying developmental genetics. In recent years several laboratories have systematically gathered confocal microscopy images of patterns of activity expression for genes governing early Drosophila development. Due to both the high variability between fruit fly embryos and diverse sources of observational errors some new nontrivial procedures for processing and integrating the raw observations are required. Here we describe processing techniques based on genetic algorithms and discuss their efficacy in decreasing observational errors and illuminating the natural variability in gene expression patterns. The specific developmental problem studied is anteroposterior specification of the body plan. Keywords and phrases image processing elastic deformations genetic algorithms observational errors variability fluctuations. 1. INTRODUCTION Functional genomics is an emerging field within biology aimed at deciphering how the .
