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On the Monte-Carlo expectation maximization for finding motifs in DNA sequences
Abstract:
Finding conserved locations or motifs in genomic sequences is of paramount importance. Expectation maximization (EM)-based algorithms are widely employed to solve motif finding problems. The present study proposes a novel initialization technique and model-shifting scheme for Monte-Carlo-based EM methods for motif finding. Two popular EM-based motif finding algorithms are compared to the proposed method, which offers improved motif prediction accuracy on a synthetic dataset and a true biological dataset.
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