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.
Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., et al. (2016). Methods for the integration of multi-omics data: Mathematical aspects. BMC BIOINFORMATICS, 17 Suppl 2(S2), 15-25 [10.1186/s12859-015-0857-9].
Methods for the integration of multi-omics data: Mathematical aspects
BERSANELLI, MATTEO;REMONDINI, DANIEL;GIAMPIERI, ENRICO;SALA, CLAUDIA;CASTELLANI, GASTONE;
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.| File | Dimensione | Formato | |
|---|---|---|---|
|
Methods multi-omic data_Bersanelli Remondini_BMCbioinfo15.pdf
accesso aperto
Tipo:
Versione (PDF) editoriale
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione
701.04 kB
Formato
Adobe PDF
|
701.04 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
