Assessment of network inference methods: how to cope with an underdetermined problem.

The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the perfor...

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Autores principales:	Caroline Siegenthaler, Rudiyanto Gunawan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0fcfd6092d3c4b17a8ff9f265e986c91
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spelling	oai:doaj.org-article:0fcfd6092d3c4b17a8ff9f265e986c912021-11-18T08:29:21ZAssessment of network inference methods: how to cope with an underdetermined problem.1932-620310.1371/journal.pone.0090481https://doaj.org/article/0fcfd6092d3c4b17a8ff9f265e986c912014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24603847/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique) solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN) inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.Caroline SiegenthalerRudiyanto GunawanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e90481 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Caroline Siegenthaler Rudiyanto Gunawan Assessment of network inference methods: how to cope with an underdetermined problem.
description	The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique) solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN) inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.
format	article
author	Caroline Siegenthaler Rudiyanto Gunawan
author_facet	Caroline Siegenthaler Rudiyanto Gunawan
author_sort	Caroline Siegenthaler
title	Assessment of network inference methods: how to cope with an underdetermined problem.
title_short	Assessment of network inference methods: how to cope with an underdetermined problem.
title_full	Assessment of network inference methods: how to cope with an underdetermined problem.
title_fullStr	Assessment of network inference methods: how to cope with an underdetermined problem.
title_full_unstemmed	Assessment of network inference methods: how to cope with an underdetermined problem.
title_sort	assessment of network inference methods: how to cope with an underdetermined problem.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0fcfd6092d3c4b17a8ff9f265e986c91
work_keys_str_mv	AT carolinesiegenthaler assessmentofnetworkinferencemethodshowtocopewithanunderdeterminedproblem AT rudiyantogunawan assessmentofnetworkinferencemethodshowtocopewithanunderdeterminedproblem
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Assessment of network inference methods: how to cope with an underdetermined problem.

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