Methods for the integration of multi-omics data: mathematical aspects
Open accessAbstract
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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Only a handful of tools and methods are currently available to investigate multi-omics data.
“Only a handful of tools and methods are currently available to investigate multi-omics data”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [177]
The Conclusions (page unavailable) say that many established multi-layer approaches exist for genomics and transcriptomics, contrary to the claim that only a handful of methods are available for multi-omics data. The paper notes narrower coverage for some other omics and gaps in software availability, but does not describe methods overall as limited to a handful. (LLM, conf=0.85)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe tools that exist for multi-omics data mostly integrate only two layers of omics data.
“the tools that exist mostly integrate only two layers of omics data [177]”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [177]
The Network-free non-bayesian section describes Integromics as integrating two types of omics, so the paper supports that some tools integrate two layers. But it also describes methods that integrate several datasets, and neither that section nor the Conclusions establishes that most existing tools integrate only two layers. Page numbers are unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMachine learning is now used on multi-omics data to study and interpret links between data and phenotypes.
“ML methods are now also being applied to with multi-omics data (Bersanelli et al., 2016), to investigate and interpret the relationships between data and phenotypes (Kim and Tagkopoulos, 2018).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Bersanelli et al., 2016
Multi-omic data from different high-throughput sources are typically heterogeneous.
“Multi-omic data from different high-throughput sources are usually heterogeneous (Bersanelli et al., 2016).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Bersanelli et al., 2016
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“Some reviews focus on differentiating general data-driven integration approaches from a mathematical perspective (e.g., Ritchie et al., 2015; Bersanelli et al., 2016; Noor et al., 2019), focus on a particular type of method, such as dimension reduction (Meng et al.”
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- Authors
- Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L.
- Affiliations
- Institute of Biomedical Technologies (IT)
- University of Bologna (IT)
- Corresponding author
- Milanesi, Luciano
- ORCID
- Venue
- BMC Bioinformatics
- Published
- 2016-01-20
- Volume
- 17
- Issue
- S2
- Pages
- 15-15
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4959355
- OpenAlex
- W2264481086
- Cited by
- 449
- References
- 54
- Citations by year
- 2026: 31 · 2025: 46 · 2024: 54 · 2023: 40 · 2022: 40 · 2021: 57 · 2020: 52 · 2019: 53 · 2018: 36 · 2017: 34 · 2016: 4 · 2015: 1
- FWCI
- 22.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- multi-omics integration, integrative analysis, mathematical methods, high-dimensional data
- MeSH terms
- Algorithms, Bayes Theorem, Humans, Models, Genetic, Software, Least-Squares Analysis, Genomics
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