AN EFFICIENT BICLUSTERING ALGORITHM FOR FINDING GENES WITH SIMILAR PATTERNS IN TIME-SERIES EXPRESSION DATA
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- TL;DR
- Biclustering algorithms are key tools for discovering local patterns in gene expression data, with efficient methods already existing for time-series data using discretized expression matrices.
- Problem
- Current efficient algorithms assume that biclusters are perfect, meaning every gene in a bicluster must exhibit the exact same expression pattern across the included conditions.
- Method
- We propose a new algorithm that identifies genes with similar, yet not necessarily identical, expression patterns over a subset of conditions.
- Results
- Experiments show that this approach uncovers biclusters with greater biological significance than those found by existing literature algorithms.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- Allowing for flexible gene expression patterns rather than requiring identical ones leads to biologically more meaningful discoveries in time-series data.
- Applications
- Not specified in the abstract.
- Topics
- Biclustering algorithms, gene expression data, time-series analysis
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
Biclustering algorithms have emerged as an important tool for the discovery of local patterns in gene expression data. For the case where the expression data corresponds to time-series, efficient algorithms that work with a discretized version of the expression matrix are known. However, these algorithms assume that the biclusters to be found are perfect, in the sense that each gene in the bicluster exhibits exactly the same expression pattern along the conditions that belong to it. In this work, we propose an algorithm that identifies genes with similar, but not necessarily equal, expression patterns, over a subset of the conditions. The results demonstrate that this approach identifies biclusters biologically more significant than those discovered by other algorithms in the literature. 1.