Biclustering Gene Expression Data with Reactive GRASP
Author Information
Author(s): Dharan Smitha, Nair Achuthsankar S
Primary Institution: Centre for Bioinformatics, University of Kerala
Hypothesis
Can a new variant of the Greedy Randomized Adaptive Search Procedure (Reactive GRASP) effectively detect significant biclusters in gene expression data?
Conclusion
The Reactive GRASP approach for detecting significant biclusters is robust and does not require calibration efforts.
Supporting Evidence
- The Reactive GRASP method outperforms the basic GRASP algorithm.
- The biclusters generated by Reactive GRASP are statistically significant.
- The method does not require calibration efforts, making it more user-friendly.
- The approach successfully identifies biologically relevant biclusters.
Takeaway
This study created a new method to find groups of genes that behave similarly under certain conditions, which helps scientists understand gene functions better.
Methodology
The study used a two-step procedure involving k-means clustering for seed generation and Reactive GRASP for bicluster refinement.
Participant Demographics
Yeast Saccharomyces cerevisiae cell cycle expression dataset with 2884 genes and 17 experimental conditions.
Statistical Information
P-Value
2.8479e-020
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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