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Functional data analysis of "Omics" data: how does the genomic landscape influence integration and fixation of endogenous retroviruses?
Penn State University.
Penn State University.
Politecnico di Milano.ORCID iD: 0000-0001-9235-3062
Politecnico di Milano.
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2017 (English)In: Functional statistics and related fields / [ed] Germán Aneiros, Enea G. Bongiorno, Ricardo Cao, Philippe Vieu, Springer, 2017, p. 87-93Chapter in book (Refereed)
Abstract [en]

 We consider thousands of endogenous retrovirus detected in the human and mouse genomes, and quantify a large number of genomic landscape features at high resolution around their integration sites and in control regions. We propose to analyze this data employing a recently developed functional inferential procedure and functional logistic regression, with the aim of gaining insights on the effects of genomic landscape features on the integration and fixation of endogenous retroviruses.

Place, publisher, year, edition, pages
Springer, 2017. p. 87-93
Series
Contributions to Statistics, ISSN 1431-1968
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-141353DOI: 10.1007/978-3-319-55846-2_12ISBN: 978-3-319-55846-2 (electronic)ISBN: 978-3-319-55845-5 (print)OAI: oai:DiVA.org:umu-141353DiVA, id: diva2:1153509
Note

Fourth International Workshop on Functional and Operatorial Statistics (IWFOS 2017), A Coruña, Spain, 15-17 June 2017.

Available from: 2017-10-30 Created: 2017-10-30 Last updated: 2018-06-09Bibliographically approved

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Pini, Alessia

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