Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Gene Specific Co-regulation Discovery: An Improved Approach
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Efficiently mining time-delayed gene expression patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining time-delayed coherent patterns in time series gene expression data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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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 follows: After normalization, the trajectories were projected on to CosBell wavelet basis. The four genes in group RH form two clusters: three of them stayed close to each other, and the last one, CG8651 (trithorax), was singled out. The group H genes, forming four clusters, showed functional features that are more similar to trithorax than the other three. We further analyzed ten homeodomain genes that have good correlations with trithorax in the wavelet basis. Literature search showed that there are five genes thought to be in the downstream pathway of trithorax. Although only two of these five genes were in the dataset available to the algorithm, it was able to identify both of these. Our study suggests that timefrequency analysis provides a powerful tool for discovering the underlying regulatory networks when applied to time-course microarray data.