Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
An algorithm for finding novel gapped motifs in DNA sequences
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Finding similar regions in many strings
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Detecting Motifs from Sequences
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
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
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Biologists have determined that the control and regulation of gene expression is primarily determined by relatively short sequences in the region surrounding a gene. These sequences vary in length, position, redundancy, orientation, and bases. Finding these short sequences is a fundamental problem in molecular biology with important applications. Though there exist many different approaches to signal/motif (i.e. short sequence) finding, in 2000 Pevzner and Sze reported that most current motif finding algorithms are incapable of detecting the target signals in their so-called Challenge Problem. In this paper, we show that using an iterative-restart design, our new algorithm can correctly find the targets. Furthermore, taking into account the fact that some transcription factors form a dimer or even more complex structures, and transcription process can sometimes involve multiple factors, we extend the original problem to an even more challenging one. We address the issue of combinatorial signals with gaps of variable lengths. To demonstrate the efficacy of our algorithm, we tested it on a series of the original and the new challenge problems, and compared it with some representative motif-finding algorithms. In addition, to verify its feasibility in real-world applications, we also tested it on several regulatory families of yeast genes with known motifs. The purpose of this paper is two-fold. One is to introduce an improved biological data mining algorithm that is capable of dealing with more variable regulatory signals in DNA sequences. The other is to propose a new research direction for the general KDD community.