Publicação
Enabling network inference methods to handle missing data and outliers
| 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. |
|---|---|
| Autores principais: | Folch-Fortuny, Abel |
| Outros Autores: | Villaverde, Alejandro F.; Ferrer, Alberto; Banga, Julio R. |
| Assunto: | Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866876385014841344 |
|---|---|
| author | Folch-Fortuny, Abel |
| author2 | Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
| author2_role | author author author |
| author_facet | Folch-Fortuny, Abel Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Folch-Fortuny, Abel\"},{\"Person.name\":\"Villaverde, Alejandro F.\"},{\"Person.name\":\"Ferrer, Alberto\"},{\"Person.name\":\"Banga, Julio R.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Folch-Fortuny, Abel Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
| datacite.date.Accepted.fl_str_mv | 2015-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2015-09-10T14:36:34Z |
| datacite.date.embargoed.fl_str_mv | 2015-09-10T14:36:34Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information |
| datacite.titles.title.fl_str_mv | Enabling network inference methods to handle missing data and outliers |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Folch-Fortuny, Abel Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
| dc.date.Accepted.fl_str_mv | 2015-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2015-09-10T14:36:34Z |
| dc.date.embargoed.fl_str_mv | 2015-09-10T14:36:34Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/37041 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | BioMed Central (BMC) |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information |
| dc.title.fl_str_mv | Enabling network inference methods to handle missing data and outliers |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/797ef8c2-05dc-4710-9ad4-6d82fbed271f/download |
| id | rum_ac08099e2c02f8d6360ca62e6dd8c3cd |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/37041 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/37041 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Folch-Fortuny, Abel Villaverde, Alejandro F. Ferrer, Alberto Banga, Julio R. |
| publishDate | 2015 |
| publisher.none.fl_str_mv | BioMed Central (BMC) |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engBioMed Central (BMC)porThe 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.application/pdfporEnabling network inference methods to handle missing data and outliersFolch-Fortuny, AbelVillaverde, Alejandro F.Ferrer, AlbertoBanga, Julio R.HostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf1471-2105DOIIsPartOf10.1186/s12859-015-0717-72015-09-10T14:36:34Z20152015-09-07T13:35:35Z2015-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/37041http://purl.org/coar/access_right/c_abf2open accessNetwork inferenceMissing dataOutlier detectionProjection to latent structuresTrimmed scores regressionInformation theoryMutual information873026 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/797ef8c2-05dc-4710-9ad4-6d82fbed271f/download |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information |
| title | 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_short | Enabling network inference methods to handle missing data and outliers |
| title_sort | Enabling network inference methods to handle missing data and outliers |
| topic | Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information |
| topic_facet | Network inference Missing data Outlier detection Projection to latent structures Trimmed scores regression Information theory Mutual information |
| url | https://hdl.handle.net/1822/37041 |
| visible | 1 |