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KDNuggets
New
clustering method for gene expressions using KL similarity
measure
"A new approach to identifying patterns in gene expression analysis has been shown to be more effective than the most popular method in a joint Penn State and University at Buffalo study. Using two published gene expression data sets as test cases, the research team found that the KL clustering method, which uses a novel measure of similarity (Kullback-Leibler) not previously used for gene expression analysis, was superior to the most popular method, hierarchical clustering, in separating the data into dense clusters with similar patterns."
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“Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.”
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