Study of microarray time series data based on Forward-Backward Linear Prediction and Singular Value Decomposition

Authors:
Miew Keen Choong;David Levy;Hong Yan
Affiliations:
School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia.;School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia.;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong/ School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2009

Citing 8
Cited 1

The SVD and reduced rank signal processing

Signal Processing - Theme issue on singular value decomposition
Numerical recipes in C (2nd ed.): the art of scientific computing

Numerical recipes in C (2nd ed.): the art of scientific computing
A Maximum Entropy Algorithm for Rhythmic Analysis of Genome-Wide Expression Patterns

CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Identifying periodically expressed transcripts in microarray time series data

Bioinformatics
Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data

Bioinformatics
Dominant spectral component analysis for transcriptional regulations using microarray time-series data

Bioinformatics
Comparison of computational methods for the identification of cell cycle-regulated genes

Bioinformatics
Spectral similarity for analysis of DNA microarray time-series data

International Journal of Data Mining and Bioinformatics

Autoregressive-model-based missing value estimation for DNA microarray time series data

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a method to analyse the periodicities of gene expression profiles based on the spectral domain approach. Our spectral reconstruction method outperforms three other recently proposed methods, which do not require any prior knowledge. It is proven that an alternative method for studying cell-cycle regulation is possible even where very little prior knowledge is available. We also investigate the potential of combining signals with similar frequency components to form an overdetermined system of equations, and use least squares solution to estimate the spectral frequency. Results show that this new method is able to estimate the peak frequency more accurately.