Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster.
AuthorCampos, TL; Korhonen, PK; Hofmann, A; Gasser, RB; Young, ND
Source TitleNAR Genom Bioinform
PublisherOxford University Press (OUP)
Document TypeJournal Article
CitationsCampos, 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 Genom Bioinform, 2 (3), pp.lqaa051-. https://doi.org/10.1093/nargab/lqaa051.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671374
Characterizing 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.
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