A new approach to text searching
Communications of the ACM
Fast text searching: allowing errors
Communications of the ACM
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Statistical Method for Finding Transcription Factor Binding Sites
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
An O(N^2) Algorithm for Discovering Optimal Boolean Pattern Pairs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In this paper, we consider the problem of extracting multiple unordered short motifs in upstream regions of given genes. Multiple unordered short motifs can be considered as a set of short motifs, say M = {m1, m2,..., mk}. For a gene g, if each of the motifs m1, .... ,mk occurs in either the upstream region or its complement of g, the gene g is said to be consistent with M. We have developed a fast method to exhaustively search collections of short motifs over given short motifs for a particular set of genes, and rank collections with using multiple objective functions. This method is implemented by employing bit operations in the process of matching short motifs with upstream regions, and identifying the members of genes which are consistent with short motifs. On various putatively co-regulated genes of Sacchrormyces cerevisiae, determined by gene expression profiles, our computational experiments show biologically interesting results.