Sarwar Tapan

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It is important to identify DNA motifs in promoter regions to understand the mechanism of gene regulation. Computational approaches for finding DNA motifs are well recognized as useful tools to biologists, which greatly help in saving experimental time and cost in wet laboratories. Self-organizing maps (SOMs), as a powerful clustering tool, have(More)
Computational approaches for finding DNA regulatory motifs in promoter sequences are useful to biologists in terms of reducing the experimental costs and speeding up the discovery process of de novo binding sites. It is important for rule-based or clustering-based motif searching schemes to effectively and efficiently evaluate the similarity between a k-mer(More)
Data-structure preserved visualization of high-dimensional data reveals the dataset borders and the spread and overlapping tendency of the class borders in a more informative manner than the usual data-topology preserved mapping produced by Self-Organizing Maps (SOMs). Hence, an extension of SOM called Probabilistic Regular-ized SOM (PRSOM) is proposed for(More)
Self-organizing map (SOM)-based motif mining, despite being a promising approach for problem solving, mostly fails to offer a consistent interpretation of clusters with respect to the mixed composition of signal and noise in the nodes. The main reason behind this shortcoming comes from the similarity metrics used in data assignment, specially designed with(More)
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