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An Entropy-Based Position Projection Algorithm for Motif Discovery
Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5110948/ https://www.ncbi.nlm.nih.gov/pubmed/27882329 http://dx.doi.org/10.1155/2016/9127474 |
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author | Zhang, Yipu Wang, Ping Yan, Maode |
author_facet | Zhang, Yipu Wang, Ping Yan, Maode |
author_sort | Zhang, Yipu |
collection | PubMed |
description | Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms. |
format | Online Article Text |
id | pubmed-5110948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-51109482016-11-23 An Entropy-Based Position Projection Algorithm for Motif Discovery Zhang, Yipu Wang, Ping Yan, Maode Biomed Res Int Research Article Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms. Hindawi Publishing Corporation 2016 2016-11-02 /pmc/articles/PMC5110948/ /pubmed/27882329 http://dx.doi.org/10.1155/2016/9127474 Text en Copyright © 2016 Yipu Zhang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Yipu Wang, Ping Yan, Maode An Entropy-Based Position Projection Algorithm for Motif Discovery |
title | An Entropy-Based Position Projection Algorithm for Motif Discovery |
title_full | An Entropy-Based Position Projection Algorithm for Motif Discovery |
title_fullStr | An Entropy-Based Position Projection Algorithm for Motif Discovery |
title_full_unstemmed | An Entropy-Based Position Projection Algorithm for Motif Discovery |
title_short | An Entropy-Based Position Projection Algorithm for Motif Discovery |
title_sort | entropy-based position projection algorithm for motif discovery |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5110948/ https://www.ncbi.nlm.nih.gov/pubmed/27882329 http://dx.doi.org/10.1155/2016/9127474 |
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