tailieunhanh - Báo cáo hóa học: " Autoregressive Modeling and Feature Analysis of DNA Sequences"

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: Autoregressive Modeling and Feature Analysis of DNA Sequences | EURASIP Journal on Applied Signal Processing 2004 1 13-28 2004 Hindawi Publishing Corporation Autoregressive Modeling and Feature Analysis of DNA Sequences Niranjan Chakravarthy Department of Electrical Engineering Arizona State University Tempe AZ 85287-5706 USA Email A. Spanias Department of Electrical Engineering Arizona State University Tempe AZ 85287-5706 USA Email spanias@ L. D. Iasemidis Harrington Department of Bioengineering Arizona State University Tempe AZ 85287-9709 USA Email K. Tsakalis Department of Electrical Engineering Arizona State University Tempe AZ 85287-5706 USA Email tsakalis@ Received 28 February 2003 Revised 15 September 2003 A parametric signal processing approach for DNA sequence analysis based on autoregressive AR modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicates a high specificity of coding DNA sequences while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in the proposed method is demonstrated. Keywords and phrases DNA autoregressive modeling feature analysis. 1. INTRODUCTION The complete understanding of cell functionalities depends primarily on the various cell activities carried out by proteins. Information for the formation and activity of these proteins is coded in the deoxyribonucleic acid DNA sequences. For detection purposes the vast amount of genomic data makes it necessary to define models for DNA segments such as the protein coding regions. Such models can also facilitate our understanding of the stored information and could provide a basis for the functional analysis of the DNA. Since the DNA is a discrete sequence it can be interpreted as a discrete categorical or symbolic sequence and hence digital signal processing