Time-frequency feature detection for time-course microarray data

Gene clustering based on microarray data provides useful functional information to the working biologists. Many current gene-clustering algorithms rely on Euclidean-based distance metrics and fail to capture the time-dependent features of the data, usually corrupted by high levels of experimental noise. Here we propose an algorithm capable of dealing with the noise through a time-frequency approach and related measure of correlation between time-course expressions of different genes (trajectories). The approach makes use of fast multi-resolution feature classification algorithms and allows for the desired functional characteristics (such as phase delay, activation/repression etc.) to be enhanced and detected. We have applied our algorithm to time-course microarray data of Drosophila melanogaster (Arbeitman et al., Science, Sep 27, 2002, page 2270-2275). We examined various relations among homeodomain genes (referred to as group H) and regulators of homeodomain genes (group RH) as foll...

• Type: Conference
• Year: 2004
• Where: SAC
• Authors: Jiawu Feng, Paolo Emilio Barbano, Bud Mishra
• Keywords: Applied Computing, Homeodomain Genes, Microarray Data, SAC 2004, Time-course Microarray Data