Enabling network inference methods to handle missing data and outliers
Autor(a) principal: | |
---|---|
Data de Publicação: | 2015 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/37041 |
Resumo: | The inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference. Results Here we present an approach to (i) handle missing data and (ii) detect and correct outliers based on multivariate projection to latent structures. The method, called trimmed scores regression (TSR), enables network inference methods to analyse incomplete datasets by imputing the missing values coherently with the latent data structure. Furthermore, it substitutes the faulty values in a dataset by proper estimations. We provide an implementation of this approach, and show how it can be integrated with any network inference method as a preliminary data curation step. This functionality is demonstrated with a state of the art network inference method based on mutual information distance and entropy reduction, MIDER. Conclusion The methodology presented here enables network inference methods to analyse a large number of incomplete and faulty datasets that could not be reliably analysed so far. Our comparative studies show the superiority of TSR over other missing data approaches used by practitioners. Furthermore, the method allows for outlier detection and correction. |
id |
RCAP_051bb63b57566d0ba89102d4bd60bf34 |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/37041 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Enabling network inference methods to handle missing data and outliersNetwork inferenceMissing dataOutlier detectionProjection to latent structuresTrimmed scores regressionInformation theoryMutual informationScience & TechnologyThe inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference. Results Here we present an approach to (i) handle missing data and (ii) detect and correct outliers based on multivariate projection to latent structures. The method, called trimmed scores regression (TSR), enables network inference methods to analyse incomplete datasets by imputing the missing values coherently with the latent data structure. Furthermore, it substitutes the faulty values in a dataset by proper estimations. We provide an implementation of this approach, and show how it can be integrated with any network inference method as a preliminary data curation step. This functionality is demonstrated with a state of the art network inference method based on mutual information distance and entropy reduction, MIDER. Conclusion The methodology presented here enables network inference methods to analyse a large number of incomplete and faulty datasets that could not be reliably analysed so far. Our comparative studies show the superiority of TSR over other missing data approaches used by practitioners. Furthermore, the method allows for outlier detection and correction.Research in this study was partially supported by the European Union through project BioPreDyn (FP7-KBBE 289434), and the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grants MultiScales (DPI2011-28112-C04-02, DPI2011-28112-C04-03), and SynBioFactory (DPI2014-55276-C5-1-R, DPI2014-55276-C5-2-R). AF Villaverde also acknowledges funding from the Xunta de Galicia through an I2C postdoctoral fellowship (I2C ED481B 2014/133-0). We also gratefully acknowledge Associate Professor Francisco Arteaga for his help in the adaptation of TSR to the PCA model building context.BioMed Central (BMC)Universidade do MinhoFolch-Fortuny, AbelVillaverde, Alejandro F.Ferrer, AlbertoBanga, Julio R.20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/37041eng1471-21051471-210510.1186/s12859-015-0717-726335628http://www.biomedcentral.com/bmcbioinformaticsinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:15:13Zoai:repositorium.sdum.uminho.pt:1822/37041Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:07:39.299716Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Enabling network inference methods to handle missing data and outliers |
title |
Enabling network inference methods to handle missing data and outliers |
spellingShingle |
Enabling network inference methods to handle missing data and outliers Folch-Fortuny, Abel Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information Science & Technology |
title_short |
Enabling network inference methods to handle missing data and outliers |
title_full |
Enabling network inference methods to handle missing data and outliers |
title_fullStr |
Enabling network inference methods to handle missing data and outliers |
title_full_unstemmed |
Enabling network inference methods to handle missing data and outliers |
title_sort |
Enabling network inference methods to handle missing data and outliers |
author |
Folch-Fortuny, Abel |
author_facet |
Folch-Fortuny, Abel Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
author_role |
author |
author2 |
Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Folch-Fortuny, Abel Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
dc.subject.por.fl_str_mv |
Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information Science & Technology |
topic |
Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information Science & Technology |
description |
The inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference. Results Here we present an approach to (i) handle missing data and (ii) detect and correct outliers based on multivariate projection to latent structures. The method, called trimmed scores regression (TSR), enables network inference methods to analyse incomplete datasets by imputing the missing values coherently with the latent data structure. Furthermore, it substitutes the faulty values in a dataset by proper estimations. We provide an implementation of this approach, and show how it can be integrated with any network inference method as a preliminary data curation step. This functionality is demonstrated with a state of the art network inference method based on mutual information distance and entropy reduction, MIDER. Conclusion The methodology presented here enables network inference methods to analyse a large number of incomplete and faulty datasets that could not be reliably analysed so far. Our comparative studies show the superiority of TSR over other missing data approaches used by practitioners. Furthermore, the method allows for outlier detection and correction. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/37041 |
url |
http://hdl.handle.net/1822/37041 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1471-2105 1471-2105 10.1186/s12859-015-0717-7 26335628 http://www.biomedcentral.com/bmcbioinformatics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
BioMed Central (BMC) |
publisher.none.fl_str_mv |
BioMed Central (BMC) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799132496142532608 |