Cargando…
Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms
The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The pe...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5270450/ https://www.ncbi.nlm.nih.gov/pubmed/28194163 http://dx.doi.org/10.1186/s13637-014-0013-2 |
_version_ | 1782501190705086464 |
---|---|
author | Fang, Jie Ouyang, Hongjia Shen, Liangzhong Dougherty, Edward R Liu, Wenbin |
author_facet | Fang, Jie Ouyang, Hongjia Shen, Liangzhong Dougherty, Edward R Liu, Wenbin |
author_sort | Fang, Jie |
collection | PubMed |
description | The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated via two metrics: the normalized-edge Hamming distance and the steady-state distribution distance. Results for synthetic networks and a well-studied budding-yeast cell cycle network show that MDL-based filtering is more effective than filtering based on conditional mutual information (CMI). In addition, MDL-based filtering provides better inference than the MDL algorithm itself. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13637-014-0013-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5270450 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-52704502017-02-13 Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms Fang, Jie Ouyang, Hongjia Shen, Liangzhong Dougherty, Edward R Liu, Wenbin EURASIP J Bioinform Syst Biol Research The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated via two metrics: the normalized-edge Hamming distance and the steady-state distribution distance. Results for synthetic networks and a well-studied budding-yeast cell cycle network show that MDL-based filtering is more effective than filtering based on conditional mutual information (CMI). In addition, MDL-based filtering provides better inference than the MDL algorithm itself. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13637-014-0013-2) contains supplementary material, which is available to authorized users. Springer International Publishing 2014-07-03 /pmc/articles/PMC5270450/ /pubmed/28194163 http://dx.doi.org/10.1186/s13637-014-0013-2 Text en © Fang et al.; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Fang, Jie Ouyang, Hongjia Shen, Liangzhong Dougherty, Edward R Liu, Wenbin Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
title | Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
title_full | Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
title_fullStr | Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
title_full_unstemmed | Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
title_short | Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
title_sort | using the minimum description length principle to reduce the rate of false positives of best-fit algorithms |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5270450/ https://www.ncbi.nlm.nih.gov/pubmed/28194163 http://dx.doi.org/10.1186/s13637-014-0013-2 |
work_keys_str_mv | AT fangjie usingtheminimumdescriptionlengthprincipletoreducetherateoffalsepositivesofbestfitalgorithms AT ouyanghongjia usingtheminimumdescriptionlengthprincipletoreducetherateoffalsepositivesofbestfitalgorithms AT shenliangzhong usingtheminimumdescriptionlengthprincipletoreducetherateoffalsepositivesofbestfitalgorithms AT doughertyedwardr usingtheminimumdescriptionlengthprincipletoreducetherateoffalsepositivesofbestfitalgorithms AT liuwenbin usingtheminimumdescriptionlengthprincipletoreducetherateoffalsepositivesofbestfitalgorithms |