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Discovering graphical Granger causality using the truncating lasso penalty
Motivation: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes i...
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935442/ https://www.ncbi.nlm.nih.gov/pubmed/20823316 http://dx.doi.org/10.1093/bioinformatics/btq377 |
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author | Shojaie, Ali Michailidis, George |
author_facet | Shojaie, Ali Michailidis, George |
author_sort | Shojaie, Ali |
collection | PubMed |
description | Motivation: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem in systems biology. Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used to discover regulatory interactions among genes. Results: In this article, we propose a novel penalization method, called truncating lasso, for estimation of causal relationships from time-course gene expression data. The proposed penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. Moreover, the resulting estimate provides information on the time lag between activation of transcription factors and their effects on regulated genes. We provide an efficient algorithm for estimation of model parameters, and show that the proposed method can consistently discover causal relationships in the large p, small n setting. The performance of the proposed model is evaluated favorably in simulated, as well as real, data examples. Availability: The proposed truncating lasso method is implemented in the R-package ‘grangerTlasso’ and is freely available at http://www.stat.lsa.umich.edu/∼shojaie/ Contact: shojaie@umich.edu |
format | Text |
id | pubmed-2935442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-29354422010-09-08 Discovering graphical Granger causality using the truncating lasso penalty Shojaie, Ali Michailidis, George Bioinformatics Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium Motivation: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem in systems biology. Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used to discover regulatory interactions among genes. Results: In this article, we propose a novel penalization method, called truncating lasso, for estimation of causal relationships from time-course gene expression data. The proposed penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. Moreover, the resulting estimate provides information on the time lag between activation of transcription factors and their effects on regulated genes. We provide an efficient algorithm for estimation of model parameters, and show that the proposed method can consistently discover causal relationships in the large p, small n setting. The performance of the proposed model is evaluated favorably in simulated, as well as real, data examples. Availability: The proposed truncating lasso method is implemented in the R-package ‘grangerTlasso’ and is freely available at http://www.stat.lsa.umich.edu/∼shojaie/ Contact: shojaie@umich.edu Oxford University Press 2010-09-15 2010-09-04 /pmc/articles/PMC2935442/ /pubmed/20823316 http://dx.doi.org/10.1093/bioinformatics/btq377 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium Shojaie, Ali Michailidis, George Discovering graphical Granger causality using the truncating lasso penalty |
title | Discovering graphical Granger causality using the truncating lasso penalty |
title_full | Discovering graphical Granger causality using the truncating lasso penalty |
title_fullStr | Discovering graphical Granger causality using the truncating lasso penalty |
title_full_unstemmed | Discovering graphical Granger causality using the truncating lasso penalty |
title_short | Discovering graphical Granger causality using the truncating lasso penalty |
title_sort | discovering graphical granger causality using the truncating lasso penalty |
topic | Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935442/ https://www.ncbi.nlm.nih.gov/pubmed/20823316 http://dx.doi.org/10.1093/bioinformatics/btq377 |
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