tailieunhanh - Báo cáo hóa học: " Clustering Time Series Gene Expression Data Based on Sum-of-Exponentials Fitting"

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: Clustering Time Series Gene Expression Data Based on Sum-of-Exponentials Fitting | EURASIP Journal on Applied Signal Processing 2005 8 1159-1173 2005 Hindawi Publishing Corporation Clustering Time Series Gene Expression Data Based on Sum-of-Exponentials Fitting Ciprian Doru Giurcaneanu Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland Email Ioan Tabus Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland Email Jaakko Astola Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland Email Received 8 June 2004 Revised 26 October 2004 Recommended for Publication by Xiaodong Wang This paper presents a method based on fitting a sum-of-exponentials model to the nonuniformly sampled data for clustering the time series of gene expression data. The structure of the model is estimated by using the minimum description length MDL principle for nonlinear regression in a new form incorporating a normalized maximum-likelihood NML model for a subset of the parameters. The performance of the structure estimation method is studied using simulated data and the superiority of the new selection criterion over earlier criteria is demonstrated. The accuracy of the nonlinear estimates of the model parameters is analyzed with respect to the Cramer-Rao lower bounds. Clustering examples of gene expression data sets from a developmental biology application are presented revealing gene grouping into clusters according to functional classes. Keywords and phrases nonuniformly sampled data sum-of-exponentials model normalized maximum likelihood time series clustering gene expression data developmental biology. 1. INTRODUCTION The gene expression time profiles are a rich source of information about the dynamics of the underlying genomic network. The experiments are often taken at nonuniform time points suggested by the biologist s intuition about the time scale of the important .