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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Vydáno v:	Computers in biology and medicine Ročník 165; s. 107425
Hlavní autoři:	Manochkumar, Janani, Cherukuri, Aswani Kumar, Kumar, Raju Suresh, Almansour, Abdulrahman I., Ramamoorthy, Siva, Efferth, Thomas
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.10.2023 Elsevier Limited
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	0010-4825 1879-0534 1879-0534
DOI:	10.1016/j.compbiomed.2023.107425