A critical review of machine-learning for “multi-omics” marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel sec...

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Veröffentlicht in:Computers in biology and medicine Jg. 165; S. 107425
Hauptverfasser: Manochkumar, Janani, Cherukuri, Aswani Kumar, Kumar, Raju Suresh, Almansour, Abdulrahman I., Ramamoorthy, Siva, Efferth, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.10.2023
Elsevier Limited
Schlagworte:
Algorithms
Antibiotics
Bioactive compounds
Biological activity
Biological analysis
Data integration
Datasets
Drug dosages
Environmental conditions
Genes
Genomes
Genomics
Internal Medicine
Learning algorithms
Machine learning
Marine organisms
Metabolism
Metabolites
Metabolomics
Multi-omics
Natural products
Organisms
Other
Proteins
Proteomics
R&D
Research & development
Secondary metabolites
Support vector machines
Transcriptomics
Metabolomics
Transcriptomics
Genomics
Multi-omics
Proteomics
Machine learning
ISSN:0010-4825, 1879-0534, 1879-0534
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available “-omics” and “multi-omics” approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for “multi-omics” approaches is briefly discussed. In addition, the challenges involved in the analysis of “multi-omics” data and recommendations for conducting “multi-omics” study were discussed. •Recent progress in the use and integration of “multi-omics” techniques to identify novel marine metabolites.•The multi-omics data integration tools developed for analyzing “multi-omics” data.•The requirement of ML for analyzing “multi-omics” data from marine metabolites.•Challenges associated with the integration of “multi-omics” data.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2023.107425