Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.
Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5293552?pdf=render |
| _version_ | 1818270981544738816 |
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| author | Zahra Narimani Hamid Beigy Ashar Ahmad Ali Masoudi-Nejad Holger Fröhlich |
| author_facet | Zahra Narimani Hamid Beigy Ashar Ahmad Ali Masoudi-Nejad Holger Fröhlich |
| author_sort | Zahra Narimani |
| collection | DOAJ |
| description | Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods. |
| first_indexed | 2024-12-12T21:18:55Z |
| format | Article |
| id | doaj.art-e3b355f37a4141fe8fea46f6d9ab24f4 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T21:18:55Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e3b355f37a4141fe8fea46f6d9ab24f42022-12-22T00:11:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01122e017124010.1371/journal.pone.0171240Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.Zahra NarimaniHamid BeigyAshar AhmadAli Masoudi-NejadHolger FröhlichInferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.http://europepmc.org/articles/PMC5293552?pdf=render |
| spellingShingle | Zahra Narimani Hamid Beigy Ashar Ahmad Ali Masoudi-Nejad Holger Fröhlich Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. PLoS ONE |
| title | Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. |
| title_full | Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. |
| title_fullStr | Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. |
| title_full_unstemmed | Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. |
| title_short | Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. |
| title_sort | expectation propagation for large scale bayesian inference of non linear molecular networks from perturbation data |
| url | http://europepmc.org/articles/PMC5293552?pdf=render |
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