An Affinity Propagation-Based DNA Motif Discovery Algorithm
The planted (l,d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challen...
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2015-01-01
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Series: | BioMed Research International |
Online Access: | http://dx.doi.org/10.1155/2015/853461 |
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doaj-0aa0dd18bdc9473d95c81c43cf653abf2020-11-24T23:29:22ZengHindawi LimitedBioMed Research International2314-61332314-61412015-01-01201510.1155/2015/853461853461An Affinity Propagation-Based DNA Motif Discovery AlgorithmChunxiao Sun0Hongwei Huo1Qiang Yu2Haitao Guo3Zhigang Sun4School of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaThe planted (l,d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.http://dx.doi.org/10.1155/2015/853461 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chunxiao Sun Hongwei Huo Qiang Yu Haitao Guo Zhigang Sun |
spellingShingle |
Chunxiao Sun Hongwei Huo Qiang Yu Haitao Guo Zhigang Sun An Affinity Propagation-Based DNA Motif Discovery Algorithm BioMed Research International |
author_facet |
Chunxiao Sun Hongwei Huo Qiang Yu Haitao Guo Zhigang Sun |
author_sort |
Chunxiao Sun |
title |
An Affinity Propagation-Based DNA Motif Discovery Algorithm |
title_short |
An Affinity Propagation-Based DNA Motif Discovery Algorithm |
title_full |
An Affinity Propagation-Based DNA Motif Discovery Algorithm |
title_fullStr |
An Affinity Propagation-Based DNA Motif Discovery Algorithm |
title_full_unstemmed |
An Affinity Propagation-Based DNA Motif Discovery Algorithm |
title_sort |
affinity propagation-based dna motif discovery algorithm |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2015-01-01 |
description |
The planted (l,d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy. |
url |
http://dx.doi.org/10.1155/2015/853461 |
work_keys_str_mv |
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1725546016064667648 |