Wagner Meira | Search PubMed

PubMed2006

A contact map matching approach to protein structure similarity analysis.

We modeled the problem of identifying how close two proteins are structurally by measuring the dissimilarity of their contact maps. These contact maps are colored images, in which the chromatic information encodes the chemical nature of the contacts. We studied two conceptually distinct image-processing algorithms to measure the dissimilarity between these contact maps; one was a content-based image retrieval method, and the other was based on image registration. In experiments with contact maps constructed from the protein data bank, our approach was able to identify, with greater than 80% precision, instances of monomers of apolipoproteins, globins, plastocyanins, retinol binding proteins and thioredoxins, among the monomers of Protein Data Bank Select. The image registration approach was only slightly more accurate than the content-based image retrieval approach.

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PubMed2004

Parallel and distributed methods for incremental frequent itemset mining.

Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.

Algorithms↗