RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no...
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2011-01-01
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doaj-69fc43ad1ff94d968eead479b26fa5a82021-03-03T20:30:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01612e2864610.1371/journal.pone.0028646RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.Marco GrimaldiRoberto VisintainerGiuseppe JurmanRegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22216103/pdf/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marco Grimaldi Roberto Visintainer Giuseppe Jurman |
spellingShingle |
Marco Grimaldi Roberto Visintainer Giuseppe Jurman RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. PLoS ONE |
author_facet |
Marco Grimaldi Roberto Visintainer Giuseppe Jurman |
author_sort |
Marco Grimaldi |
title |
RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. |
title_short |
RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. |
title_full |
RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. |
title_fullStr |
RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. |
title_full_unstemmed |
RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks. |
title_sort |
regnann: reverse engineering gene networks using artificial neural networks. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
description |
RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22216103/pdf/?tool=EBI |
work_keys_str_mv |
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