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Higher-order partial least squares for predicting gene expression levels from chromatin states
BACKGROUND: Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modificati...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907142/ https://www.ncbi.nlm.nih.gov/pubmed/29671394 http://dx.doi.org/10.1186/s12859-018-2100-y |
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author | Sun, Shiquan Sun, Xifang Zheng, Yan |
author_facet | Sun, Shiquan Sun, Xifang Zheng, Yan |
author_sort | Sun, Shiquan |
collection | PubMed |
description | BACKGROUND: Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels. RESULTS: The prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) on three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful to predicting gene expression levels than several other popular methods. Specifically, our method enable to get more powerful performance on both commonly used criteria, R and RMSE, as high as 1.7% and 11%, respectively. CONCLUSIONS: The overall aim of this work is to show that the higher-order representation is able to include more unknown interaction information between histone modifications across different species. |
format | Online Article Text |
id | pubmed-5907142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59071422018-04-30 Higher-order partial least squares for predicting gene expression levels from chromatin states Sun, Shiquan Sun, Xifang Zheng, Yan BMC Bioinformatics Research BACKGROUND: Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels. RESULTS: The prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) on three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful to predicting gene expression levels than several other popular methods. Specifically, our method enable to get more powerful performance on both commonly used criteria, R and RMSE, as high as 1.7% and 11%, respectively. CONCLUSIONS: The overall aim of this work is to show that the higher-order representation is able to include more unknown interaction information between histone modifications across different species. BioMed Central 2018-04-11 /pmc/articles/PMC5907142/ /pubmed/29671394 http://dx.doi.org/10.1186/s12859-018-2100-y Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Sun, Shiquan Sun, Xifang Zheng, Yan Higher-order partial least squares for predicting gene expression levels from chromatin states |
title | Higher-order partial least squares for predicting gene expression levels from chromatin states |
title_full | Higher-order partial least squares for predicting gene expression levels from chromatin states |
title_fullStr | Higher-order partial least squares for predicting gene expression levels from chromatin states |
title_full_unstemmed | Higher-order partial least squares for predicting gene expression levels from chromatin states |
title_short | Higher-order partial least squares for predicting gene expression levels from chromatin states |
title_sort | higher-order partial least squares for predicting gene expression levels from chromatin states |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907142/ https://www.ncbi.nlm.nih.gov/pubmed/29671394 http://dx.doi.org/10.1186/s12859-018-2100-y |
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