Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
Deterministic annealing EM algorithm
Neural Networks
Learning mixture models using a genetic version of the EM algorithm
Pattern Recognition Letters
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
Spelling Approximate Repeated or Common Motifs Using a Suffix Tree
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
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The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences. Popular algorithms like Expectation Maximization (EM) and Gibbs sampling are sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. A novel optimization framework searches the neighborhood regions of the initial alignments in a systematic manner to explore the multiple local optimal solutions. This effective search is achieved by transforming the original optimization problem into its corresponding dynamical system and estimating the practical stability boundary of the local maximum. The work aims at implementing the hybrid algorithm and enhancing it by trying different global methods and other techniques. Then aggregation methods rather than projection methods are tried.