Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation
Permanent Atrial fibrillation (pmAF) has largely remained incurable since the existing information for explaining precise mechanisms underlying pmAF is not sufficient. Microarray analysis offers a broader and unbiased approach to identify and predict new biological features of pmAF. By considering t...
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sg-ntu-dr.10356-984802022-02-16T16:26:57Z Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation Ou, Feng Rao, Nini Jiang, Xudong Qian, Mengyao Feng, Wei Yin, Lixue Chen, Xu Xu, Ying School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Applications of electronics Permanent Atrial fibrillation (pmAF) has largely remained incurable since the existing information for explaining precise mechanisms underlying pmAF is not sufficient. Microarray analysis offers a broader and unbiased approach to identify and predict new biological features of pmAF. By considering the unbalanced sample numbers in most microarray data of case - control, we designed an asymmetric principal component analysis algorithm and applied it to re - analyze differential gene expression data of pmAF patients and control samples for predicting new biological features. Finally, we identified 51 differentially expressed genes using the proposed method, in which 42 differentially expressed genes are new findings compared with two related works on the same data and the existing studies. The enrichment analysis illustrated the reliability of identified differentially expressed genes. Moreover, we predicted three new pmAF – related signaling pathways using the identified differentially expressed genes via the KO-Based Annotation System. Our analysis and the existing studies supported that the predicted signaling pathways may promote the pmAF progression. The results above are worthy to do further experimental studies. This work provides some new insights into molecular features of pmAF. It has also the potentially important implications for improved understanding of the molecular mechanisms of pmAF. Published version 2014-01-10T02:24:46Z 2019-12-06T19:55:44Z 2014-01-10T02:24:46Z 2019-12-06T19:55:44Z 2013 2013 Journal Article Ou, F., Rao, N., Jiang, X., Qian, M., Feng, W., Yin, L., et al. (2013). Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation. PLoS ONE, 8(10), e76166-. 1932-6203 https://hdl.handle.net/10356/98480 http://hdl.handle.net/10220/18425 10.1371/journal.pone.0076166 24204599 en PLoS ONE © 2013 The Authors. This paper was published in PLoS ONE and is made available as an electronic reprint (preprint) with permission of the authors. The paper can be found at the following official DOI: [http://dx.doi.org/10.1371/journal.pone.0076166]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Applications of electronics Ou, Feng Rao, Nini Jiang, Xudong Qian, Mengyao Feng, Wei Yin, Lixue Chen, Xu Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
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Permanent Atrial fibrillation (pmAF) has largely remained incurable since the existing information for explaining precise mechanisms underlying pmAF is not sufficient. Microarray analysis offers a broader and unbiased approach to identify and predict new biological features of pmAF. By considering the unbalanced sample numbers in most microarray data of case - control, we designed an asymmetric principal component analysis algorithm and applied it to re - analyze differential gene expression data of pmAF patients and control samples for predicting new biological features. Finally, we identified 51 differentially expressed genes using the proposed method, in which 42 differentially expressed genes are new findings compared with two related works on the same data and the existing studies. The enrichment analysis illustrated the reliability of identified differentially expressed genes. Moreover, we predicted three new pmAF – related signaling pathways using the identified differentially expressed genes via the KO-Based Annotation System. Our analysis and the existing studies supported that the predicted signaling pathways may promote the pmAF progression. The results above are worthy to do further experimental studies. This work provides some new insights into molecular features of pmAF. It has also the potentially important implications for improved understanding of the molecular mechanisms of pmAF. |
author2 |
Xu, Ying |
author_facet |
Xu, Ying Ou, Feng Rao, Nini Jiang, Xudong Qian, Mengyao Feng, Wei Yin, Lixue Chen, Xu |
format |
Article |
author |
Ou, Feng Rao, Nini Jiang, Xudong Qian, Mengyao Feng, Wei Yin, Lixue Chen, Xu |
author_sort |
Ou, Feng |
title |
Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
title_short |
Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
title_full |
Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
title_fullStr |
Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
title_full_unstemmed |
Analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
title_sort |
analysis on differential gene expression data for prediction of new biological features in permanent atrial fibrillation |
publishDate |
2014 |
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https://hdl.handle.net/10356/98480 http://hdl.handle.net/10220/18425 |
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1725985511995080704 |