Methods for the integration of multi-omics data: mathematical aspects
By M. Bersanelli, E. Mosca, D. Remondini, E. Giampieri, C. Sala, G. Castellani, L. Milanesi. BMC Bioinformatics, 2016
396 citations recorded by the source corpus.
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. We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.
Subfields: Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/822efe599da2f4ff77c31e72eed56bfb25062b3f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sipko van Dam
- Urmo Võsa
- Adriaan van der Graaf
- João Pedro de Magalhães
- Olivier Périn
- Marie-Pier Scott-Boyer
- Milan Picard
- Arnaud Droit
Citations in the corpus
- Integration strategies of multi-omics data for machine learning analysis (2021, background): “In the current literature, general reviews on vertical integration [18–21] often classify methods based on mathematical aspects, usually whether or not they are Bayesian, Network-based, deep learning-based, kernel-based, or matrix factorization-based methods.”
- Gene co-expression analysis for functional classification and gene–disease predictions (2017, background): “Only a handful of tools and methods are currently available to investigate multi-omics data, and the tools that exist mostly integrate only two layers of omics data [177].”