Computing the minimum Hausdorff distance for point sets under translation
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis techniques for microarray time-series data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
A Maximum Entropy Algorithm for Rhythmic Analysis of Genome-Wide Expression Patterns
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Gene Regulatory Network modelling: a state-space approach
International Journal of Data Mining and Bioinformatics
| Hi-index | 0.00 |
We introduce a model-based analysis technique for extracting and characterizing rhythmic expression profiles from genome-wide DNA microarray hybridization data. These patterns are clues to discovering rhythmic genes implicated in cell-cycle, circadian, and other biological processes. The algorithm, implemented in a program called RAGE (Rhythmic Analysis of Gene Expression), decouples the problems of estimating a pattern's periodicity and phase. Our algorithm is linear-time in frequency and phase resolution, an improvement over previous quadratic-time approaches. Unlike previous approaches, RAGE uses a true distance metric for measuring expression profile similarity, based on the Hausdorff distance. This results in better clustering of expression profiles for rhythmic analysis. The confidence of each frequency estimate is computed using Z-scores. We demonstrate that RAGE is superior to other techniques on synthetic and actual DNA microarray hybridization data. We also show how to replace the discretized phase search in our method with an exact (combinatorially precise) phase search, resulting in a faster algorithm with no complexity dependence on phase resolution.