Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets

Research on similarity searching of cheminformatic data sets has been focused on similarity measures using fingerprints. However, nominal scales are the least informative of all metric scales, increasing the tied similarity scores, and decreasing the effectivity of the retrieval engines. Tanimoto�...

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Fecha de publicación:: 2016

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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dc.title.none.fl_str_mv	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
title	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
spellingShingle	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets Chemistry Reliability Similarity measures Sorting and searching Benchmarking Chemistry Nearest neighbor search Reliability Four-nearest-neighbors Molecular interpretation No free lunch theorem Performance metrices Proximity measure Similarity measure Similarity Searching Sorting and searching Population statistics Algorithm Chemical database Chemistry Data mining Information science Procedures Algorithms Chemistry Data mining Databases, Chemical Informatics
title_short	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
title_full	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
title_fullStr	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
title_full_unstemmed	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
title_sort	Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets
dc.subject.keywords.none.fl_str_mv	Chemistry Reliability Similarity measures Sorting and searching Benchmarking Chemistry Nearest neighbor search Reliability Four-nearest-neighbors Molecular interpretation No free lunch theorem Performance metrices Proximity measure Similarity measure Similarity Searching Sorting and searching Population statistics Algorithm Chemical database Chemistry Data mining Information science Procedures Algorithms Chemistry Data mining Databases, Chemical Informatics
topic	Chemistry Reliability Similarity measures Sorting and searching Benchmarking Chemistry Nearest neighbor search Reliability Four-nearest-neighbors Molecular interpretation No free lunch theorem Performance metrices Proximity measure Similarity measure Similarity Searching Sorting and searching Population statistics Algorithm Chemical database Chemistry Data mining Information science Procedures Algorithms Chemistry Data mining Databases, Chemical Informatics
description	Research on similarity searching of cheminformatic data sets has been focused on similarity measures using fingerprints. However, nominal scales are the least informative of all metric scales, increasing the tied similarity scores, and decreasing the effectivity of the retrieval engines. Tanimoto's coefficient has been claimed to be the most prominent measure for this task. Nevertheless, this field is far from being exhausted since the computer science no free lunch theorem predicts that "no similarity measure has overall superiority over the population of data sets". We introduce 12 relational agreement (RA) coefficients for seven metric scales, which are integrated within a group fusion-based similarity searching algorithm. These similarity measures are compared to a reference panel of 21 proximity quantifiers over 17 benchmark data sets (MUV), by using informative descriptors, a feature selection stage, a suitable performance metric, and powerful comparison tests. In this stage, RA coefficients perform favourably with repect to the state-of-the-art proximity measures. Afterward, the RA-based method outperform another four nearest neighbor searching algorithms over the same data domains. In a third validation stage, RA measures are successfully applied to the virtual screening of the NCI data set. Finally, we discuss a possible molecular interpretation for these similarity variants. © 2016 IEEE.
publishDate	2016
dc.date.issued.none.fl_str_mv	2016
dc.date.accessioned.none.fl_str_mv	2020-03-26T16:32:45Z
dc.date.available.none.fl_str_mv	2020-03-26T16:32:45Z
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dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.type.hasVersion.none.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.spa.none.fl_str_mv	Artículo
status_str	publishedVersion
dc.identifier.citation.none.fl_str_mv	IEEE/ACM Transactions on Computational Biology and Bioinformatics; Vol. 13, Núm. 1; pp. 158-167
dc.identifier.issn.none.fl_str_mv	15455963
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/9004
dc.identifier.doi.none.fl_str_mv	10.1109/TCBB.2015.2424435
dc.identifier.instname.none.fl_str_mv	Universidad Tecnológica de Bolívar
dc.identifier.reponame.none.fl_str_mv	Repositorio UTB
dc.identifier.orcid.none.fl_str_mv	24436944800 57188713140 55665599200 57193746355
identifier_str_mv	IEEE/ACM Transactions on Computational Biology and Bioinformatics; Vol. 13, Núm. 1; pp. 158-167 15455963 10.1109/TCBB.2015.2424435 Universidad Tecnológica de Bolívar Repositorio UTB 24436944800 57188713140 55665599200 57193746355
url	https://hdl.handle.net/20.500.12585/9004
dc.language.iso.none.fl_str_mv	eng
language	eng
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dc.rights.uri.none.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.rights.cc.none.fl_str_mv	Atribución-NoComercial 4.0 Internacional
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dc.format.medium.none.fl_str_mv	Recurso electrónico
dc.format.mimetype.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv	Institute of Electrical and Electronics Engineers Inc.
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institution	Universidad Tecnológica de Bolívar
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spelling	2020-03-26T16:32:45Z2020-03-26T16:32:45Z2016IEEE/ACM Transactions on Computational Biology and Bioinformatics; Vol. 13, Núm. 1; pp. 158-16715455963https://hdl.handle.net/20.500.12585/900410.1109/TCBB.2015.2424435Universidad Tecnológica de BolívarRepositorio UTB24436944800571887131405566559920057193746355Research on similarity searching of cheminformatic data sets has been focused on similarity measures using fingerprints. However, nominal scales are the least informative of all metric scales, increasing the tied similarity scores, and decreasing the effectivity of the retrieval engines. Tanimoto's coefficient has been claimed to be the most prominent measure for this task. Nevertheless, this field is far from being exhausted since the computer science no free lunch theorem predicts that "no similarity measure has overall superiority over the population of data sets". We introduce 12 relational agreement (RA) coefficients for seven metric scales, which are integrated within a group fusion-based similarity searching algorithm. These similarity measures are compared to a reference panel of 21 proximity quantifiers over 17 benchmark data sets (MUV), by using informative descriptors, a feature selection stage, a suitable performance metric, and powerful comparison tests. In this stage, RA coefficients perform favourably with repect to the state-of-the-art proximity measures. Afterward, the RA-based method outperform another four nearest neighbor searching algorithms over the same data domains. In a third validation stage, RA measures are successfully applied to the virtual screening of the NCI data set. Finally, we discuss a possible molecular interpretation for these similarity variants. © 2016 IEEE.Recurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84962028690&doi=10.1109%2fTCBB.2015.2424435&partnerID=40&md5=fbef0edaa9b5080d13f6b2c9480cf72bRelational Agreement Measures for Similarity Searching of Cheminformatic Data Setsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1ChemistryReliabilitySimilarity measuresSorting and searchingBenchmarkingChemistryNearest neighbor searchReliabilityFour-nearest-neighborsMolecular interpretationNo free lunch theoremPerformance metricesProximity measureSimilarity measureSimilarity SearchingSorting and searchingPopulation statisticsAlgorithmChemical databaseChemistryData miningInformation scienceProceduresAlgorithmsChemistryData miningDatabases, ChemicalInformaticsRivera-Borroto O.M.García-De La Vega J.M.Marrero-Ponce Y.Grau R.Maggiora, G., Shanmugasundaram, V., Molecular similarity measures (2011) Chemoinformatics and Computational Chemical Biology, pp. 77-84. , Methods in Molecular Biology, J. 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Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets

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