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Patterns of differential expression by association in omic data using a new measure based on ensemble learning

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dc.contributor.authorMartín Arevalillo, Jorge
dc.contributor.authorMartín Arevalillo, Raquel
dc.contributor.orcidhttps://orcid.org/0000-0003-0674-0053
dc.date.accessioned2025-03-18T12:50:35Z
dc.date.available2025-03-18T12:50:35Z
dc.date.issued2023-11-23
dc.descriptionThe registered version of this article, first published in “Statistical Applications in Genetics and Molecular Biology, vol. 22, 2023", is available online at the publisher's website: De Gruyter, https://doi.org/10.1515/sagmb-2023-0009 La versión registrada de este artículo, publicado por primera vez en “Statistical Applications in Genetics and Molecular Biology, vol. 22, 2023", está disponible en línea en el sitio web del editor: De Gruyter, https://doi.org/10.1515/sagmb-2023-0009
dc.description.abstractThe ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.en
dc.description.versionversión final
dc.identifier.citationJorge M Arevalillo, Raquel Martín-Arevalillo (2023). Patterns of differential expression by association in omic data using a new measure based on ensemble learning. Statistical Applications in Genetics and Molecular Biology. 22 (1): 20230009. https://doi.org/10.1515/sagmb-2023-0009
dc.identifier.doihttps://doi.org/10.1515/sagmb-2023-0009
dc.identifier.issn2194-6302 | eISSN 1544-6115
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26311
dc.journal.issue1
dc.journal.titleStatistical Applications in Genetics and Molecular Biology
dc.journal.volume22
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsomic dataen
dc.subject.keywordsdifferential expressionen
dc.subject.keywordsassociation patternsen
dc.subject.keywordsensemble learningen
dc.subject.keywordsrandom forestsen
dc.subject.keywordsout of bag error rateen
dc.titlePatterns of differential expression by association in omic data using a new measure based on ensemble learningen
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dc.typejournal articleen
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relation.isAuthorOfPublication.latestForDiscoveryea1c092a-eceb-49c8-abb7-d4d52f53930a
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