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.

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

Mosca E;Milanesi L
2016

Abstract

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.
2016
Istituto di Tecnologie Biomediche - ITB
Inglese
http://www.scopus.com/inward/record.url?eid=2-s2.0-84954446439&partnerID=q2rCbXpz
Sì, ma tipo non specificato
Data integration
Multi-omics
Omics
7
info:eu-repo/semantics/article
262
Bersanelli, M; Mosca, E; Remondini, D; Giampieri, E; Sala, C; Castellani, G; Milanesi, L
01 Contributo su Rivista::01.01 Articolo in rivista
none
   Methods for Integrated analysis of Multiple Omics datasets
   MIMOMICS
   FP7
   305280
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/342465
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