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dc.contributor.authorCampos, TL
dc.contributor.authorKorhonen, PK
dc.contributor.authorHofmann, A
dc.contributor.authorGasser, RB
dc.contributor.authorYoung, ND
dc.date.accessioned2020-11-26T23:08:52Z
dc.date.available2020-11-26T23:08:52Z
dc.date.issued2020-09-01
dc.identifierpii: lqaa051
dc.identifier.citationCampos, T. L., Korhonen, P. K., Hofmann, A., Gasser, R. B. & Young, N. D. (2020). Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster. NAR GENOMICS AND BIOINFORMATICS, 2 (3), https://doi.org/10.1093/nargab/lqaa051.
dc.identifier.issn2631-9268
dc.identifier.urihttp://hdl.handle.net/11343/252126
dc.description.abstractCharacterizing genes that are critical for the survival of an organism (i.e. essential) is important to gain a deep understanding of the fundamental cellular and molecular mechanisms that sustain life. Functional genomic investigations of the vinegar fly, Drosophila melanogaster, have unravelled the functions of numerous genes of this model species, but results from phenomic experiments can sometimes be ambiguous. Moreover, the features underlying gene essentiality are poorly understood, posing challenges for computational prediction. Here, we harnessed comprehensive genomic-phenomic datasets publicly available for D. melanogaster and a machine-learning-based workflow to predict essential genes of this fly. We discovered strong predictors of such genes, paving the way for computational predictions of essentiality in less-studied arthropod pests and vectors of infectious diseases.
dc.languageEnglish
dc.publisherOXFORD UNIV PRESS
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0
dc.titleCombined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster
dc.typeJournal Article
dc.identifier.doi10.1093/nargab/lqaa051
melbourne.affiliation.departmentVeterinary Biosciences
melbourne.source.titleNAR Genomics and Bioinformatics
melbourne.source.volume2
melbourne.source.issue3
melbourne.source.pageslqaa051-
dc.rights.licenseCC BY-NC
melbourne.elementsid1458085
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671374
melbourne.contributor.authorCampos, Tulio
melbourne.contributor.authorYoung, Neil
melbourne.contributor.authorGasser, Robin
melbourne.contributor.authorDe Lima Campos, Túlio
melbourne.contributor.authorKorhonen, Pasi
melbourne.contributor.authorHofmann, Andreas
dc.identifier.eissn2631-9268
melbourne.accessrightsOpen Access


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