Methods for the integration of multi-omics data: mathematical aspects

BACKGROUND: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number...

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Autores principales: Bersanelli, Matteo, Mosca, Ettore, Remondini, Daniel, Giampieri, Enrico, Sala, Claudia, Castellani, Gastone, Milanesi, Luciano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959355/
https://www.ncbi.nlm.nih.gov/pubmed/26821531
http://dx.doi.org/10.1186/s12859-015-0857-9
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author Bersanelli, Matteo
Mosca, Ettore
Remondini, Daniel
Giampieri, Enrico
Sala, Claudia
Castellani, Gastone
Milanesi, Luciano
author_facet Bersanelli, Matteo
Mosca, Ettore
Remondini, Daniel
Giampieri, Enrico
Sala, Claudia
Castellani, Gastone
Milanesi, Luciano
author_sort Bersanelli, Matteo
collection PubMed
description BACKGROUND: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. RESULTS AND CONCLUSIONS: We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.
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spelling pubmed-49593552016-08-01 Methods for the integration of multi-omics data: mathematical aspects Bersanelli, Matteo Mosca, Ettore Remondini, Daniel Giampieri, Enrico Sala, Claudia Castellani, Gastone Milanesi, Luciano BMC Bioinformatics Research BACKGROUND: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. RESULTS AND CONCLUSIONS: We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects. BioMed Central 2016-01-20 /pmc/articles/PMC4959355/ /pubmed/26821531 http://dx.doi.org/10.1186/s12859-015-0857-9 Text en © Bersanelli et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Bersanelli, Matteo
Mosca, Ettore
Remondini, Daniel
Giampieri, Enrico
Sala, Claudia
Castellani, Gastone
Milanesi, Luciano
Methods for the integration of multi-omics data: mathematical aspects
title Methods for the integration of multi-omics data: mathematical aspects
title_full Methods for the integration of multi-omics data: mathematical aspects
title_fullStr Methods for the integration of multi-omics data: mathematical aspects
title_full_unstemmed Methods for the integration of multi-omics data: mathematical aspects
title_short Methods for the integration of multi-omics data: mathematical aspects
title_sort methods for the integration of multi-omics data: mathematical aspects
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959355/
https://www.ncbi.nlm.nih.gov/pubmed/26821531
http://dx.doi.org/10.1186/s12859-015-0857-9
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