Detecting Motifs from Sequences
Abstract
The problem of multiple global comparison in families of biological sequences has been wellstudied. Fewer algorithms have been developed for identifying local consensus patterns or motifs in biological sequence. These two important problems have different biological constraints and, consequently, different computational approaches. The difficulty of finding the biologically meaningful motifs results from (1) the variation among motif bases, (2) the alignment of motif position (sites) among the sequences, and (3) the multiplicity of motif occurrences within a given sequence. In this paper, we review and compare the main approaches for finding motifs. We also introduce our own approach, DMS, which combines two objective functions with an improved iterative sampling search method. We demonstrate the effectiveness of the various algorithms by comparing them on 10 real domains and 14 artificial domains. The main advantage of DMS is that it is better able to find shorte...
Cite
Text
Hu et al. "Detecting Motifs from Sequences." International Conference on Machine Learning, 1999.Markdown
[Hu et al. "Detecting Motifs from Sequences." International Conference on Machine Learning, 1999.](https://mlanthology.org/icml/1999/hu1999icml-detecting/)BibTeX
@inproceedings{hu1999icml-detecting,
title = {{Detecting Motifs from Sequences}},
author = {Hu, Yuh-Jyh and Sandmeyer, Suzanne B. and Kibler, Dennis F.},
booktitle = {International Conference on Machine Learning},
year = {1999},
pages = {181-190},
url = {https://mlanthology.org/icml/1999/hu1999icml-detecting/}
}