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Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology
Open accessAbstract
Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.
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“Currently, many of the multi-omic reviews are focussed on individual sub-topics.For example, designing studies (Haas et al., 2017;Hasin et al., 2017), setting up workflows (Kohl et al., 2014), choosing software tools (Misra et al., 2019) and evaluating overfitted performance (McCabe et al., 2020).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Haas et al., 2017
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“However, there are few other challenges related to multi-omics studies which are not ML related such as study design (Haas et al., 2017), multi-site sample collection & management (Pinu et al., 2019), multi-site data sharing and governance (Saulnier et al., 2019), visualisation (Mougin et al., 2018), ethical standards (Lévesque et al., 2018), and finally making the research reproducible (Conesa”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Haas et al., 2017
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“In disease classification, certain disease classes are rarer than others which can cause a class imbalance in the multi-omics dataset (Haas et al., 2017).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Haas et al., 2017
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Metadata
- Authors
- Haas, R., Zelezniak, A., Iacovacci, J., Kamrad, S., Townsend, S., & Ralser, M.
- Affiliations
- The Francis Crick Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Ralser, Markus
- Venue
- Current Opinion in Systems Biology
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 6
- Pages
- 37-45
- ISSN
- 2452-3100 (ISSN-L)
- PMC
- PMC7477987
- OpenAlex
- W2750804603
- Cited by
- 131
- References
- 86
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 14 · 2023: 15 · 2022: 17 · 2021: 25 · 2020: 14 · 2019: 20 · 2018: 5
- FWCI
- 1.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- The Francis Crick Institute (cc-by)
- Funders
- Francis Crick Institute
- Cancer Research UK (FC001134)
- UK Medical Research Council (FC001134)
- Wellcome Trust (FC001134)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- multi-omics, biological mechanisms, data heterogeneity, proteomics, metabolomics, transcriptomics, ionomics, systems biology
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