Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization (1995)
| Venue: | Machine Learning |
| Citations: | 166 - 7 self |
BibTeX
@INPROCEEDINGS{Bailey95unsupervisedlearning,
author = {Timothy L. Bailey and Charles Elkan},
title = {Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization},
booktitle = {Machine Learning},
year = {1995},
pages = {51--80}
}
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Abstract
. The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unalignedbiopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using EM and increase the chance of finding good solutions. First, subsequences which actually occur in the biopolymer sequences are used as starting points for the EM algorithm to increase the probability of finding globally optimal motifs. Second, the assumption that each sequence contains exactly one occurrence of the shared motif is removed. This allows multiple appearances of a motif to occur in any sequence and permits the algorithm to ignore sequences with no appearance of the shared motif, increasing its resistance to noisy data. Third, a method for probabilistically erasing shared motifs after they are found is incorporated so tha...
