Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
On approximation algorithms for local multiple alignment
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Graph Theory With Applications
Graph Theory With Applications
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The identification of regulatory signals is one of the mostchallenging tasks in bioinformatics. The development ofgene-profiling technologies now makes it possible to obtainvast data on gene expression in a particular organism undervarious conditions. This has created the opportunityto identify and analyze the parts of the genome believed tobe responsible for transcription control - the transcriptionfactor DNA-binding motifs (TFBMs). Developing a practicaland efficient computational tool to identify TFBMs willenable us to better understand the interplay among thousandsof genes in a complex eukaryotic organism. Thisproblem, which is mathematically formulated as the motiffinding problem in computer science, has been studiedextensively in recent years. We develop a new mathematicalmodel and approximation technique for motif searching.Based on the graph theoretic and geometric properties ofthis approach, we propose a non-statistical approximationalgorithm to find motifs in a set of genome sequences.