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Graph machine learning for integrated multi-omics analysis

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Bibliographic Details
Title: Graph machine learning for integrated multi-omics analysis
Authors: Nektarios A. Valous, Ferdinand Popp, Inka Zörnig, Dirk Jäger, Pornpimol Charoentong
Source: Br J Cancer
Publisher Information: Springer Science and Business Media LLC, 2024.
Publication Year: 2024
Subject Terms: Machine Learning, Proteomics, 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Neoplasms, Perspective, Humans, Computational Biology, Genomics, Neural Networks, Computer, Multiomics, Proteomics/methods [MeSH], Multiomics [MeSH], 639/705/1042, Humans [MeSH], perspective, Machine Learning [MeSH], Genomics/methods [MeSH], Neoplasms/genetics [MeSH], Neural Networks, Computer [MeSH], 631/114/2401, Computational Biology/methods [MeSH]
Description: Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches—including graph neural networks—potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.
Document Type: Article
Other literature type
Language: English
ISSN: 1532-1827
0007-0920
DOI: 10.1038/s41416-024-02706-7
Access URL: https://pubmed.ncbi.nlm.nih.gov/38729996
https://repository.publisso.de/resource/frl:6501144
Rights: CC BY
Accession Number: edsair.doi.dedup.....4f126600b8852094e3a5eb87e18f72a9
Database: OpenAIRE
Description
Abstract:Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches—including graph neural networks—potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.
ISSN:15321827
00070920
DOI:10.1038/s41416-024-02706-7