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

Publication type:
Journal article
Metadata:
Autoren
  • Janani Manochkumar
  • Aswani Kumar Cherukuri
  • Raju Suresh Kumar
  • Abdulrahman I Almansour
  • Siva Ramamoorthy
  • Thomas Efferth
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:001077309000001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1016/j.compbiomed.2023.107425
eISSN
1879-0534
Externe Identifier
  • Clarivate Analytics Document Solution ID: T3VX9
  • PubMed Identifier: 37696182
ISSN
0010-4825
Zeitschrift
COMPUTERS IN BIOLOGY AND MEDICINE
Schlüsselwörter
  • Genomics
  • Transcriptomics
  • Proteomics
  • Metabolomics
  • Multi-omics
  • Machine learning
Artikelnummer
ARTN 107425
Datum der Veröffentlichung
2023
Status
Published
Titel
A critical review of machine-learning for "multi-omics" marine metabolite datasets
Sub types
  • Review
Ausgabe der Zeitschrift
165

Data source: Web of Science (Lite)

Other metadata sources:
Autoren
  • Janani Manochkumar
  • Aswani Kumar Cherukuri
  • Raju Suresh Kumar
  • Abdulrahman I Almansour
  • Siva Ramamoorthy
  • Thomas Efferth
DOI
10.1016/j.compbiomed.2023.107425
ISSN
0010-4825
Zeitschrift
Computers in Biology and Medicine
Sprache
en
Artikelnummer
107425
Paginierung
107425 - 107425
Datum der Veröffentlichung
2023
Status
Published
Herausgeber
Elsevier BV
Herausgeber URL
http://dx.doi.org/10.1016/j.compbiomed.2023.107425
Datum der Datenerfassung
2023
Titel
A critical review of machine-learning for “multi-omics” marine metabolite datasets
Ausgabe der Zeitschrift
165

Data source: Crossref

Abstract
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.
Addresses
  • School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India.
Autoren
  • Janani Manochkumar
  • Aswani Kumar Cherukuri
  • Raju Suresh Kumar
  • Abdulrahman I Almansour
  • Siva Ramamoorthy
  • Thomas Efferth
DOI
10.1016/j.compbiomed.2023.107425
eISSN
1879-0534
Externe Identifier
  • PubMed Identifier: 37696182
Funding acknowledgements
  • King Saud University:
Open access
false
ISSN
0010-4825
Zeitschrift
Computers in biology and medicine
Schlüsselwörter
  • Gene Expression Profiling
  • Proteomics
  • Algorithms
  • Machine Learning
  • Multiomics
Sprache
eng
Medium
Print-Electronic
Online publication date
2023
Paginierung
107425
Datum der Veröffentlichung
2023
Status
Published
Datum der Datenerfassung
2023
Titel
A critical review of machine-learning for "multi-omics" marine metabolite datasets.
Sub types
  • Research Support, Non-U.S. Gov't
  • Review
  • Journal Article
Ausgabe der Zeitschrift
165

Data source: Europe PubMed Central

Abstract
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.
Date of acceptance
2023
Autoren
  • Janani Manochkumar
  • Aswani Kumar Cherukuri
  • Raju Suresh Kumar
  • Abdulrahman I Almansour
  • Siva Ramamoorthy
  • Thomas Efferth
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/37696182
DOI
10.1016/j.compbiomed.2023.107425
eISSN
1879-0534
Zeitschrift
Comput Biol Med
Schlüsselwörter
  • Genomics
  • Machine learning
  • Metabolomics
  • Multi-omics
  • Proteomics
  • Transcriptomics
  • Multiomics
  • Proteomics
  • Algorithms
  • Gene Expression Profiling
  • Machine Learning
Sprache
eng
Country
United States
Paginierung
107425
PII
S0010-4825(23)00890-9
Datum der Veröffentlichung
2023
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2023
Titel
A critical review of machine-learning for "multi-omics" marine metabolite datasets.
Sub types
  • Journal Article
  • Review
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
165

Data source: PubMed

Autoren
  • Janani Manochkumar
  • Aswani Kumar Cherukuri
  • Raju Suresh Kumar
  • Abdulrahman I Almansour
  • Siva Ramamoorthy
  • Thomas Efferth
Zeitschrift
Comput. Biol. Medicine
Paginierung
107425 - 107425
Datum der Veröffentlichung
2023
Titel
A critical review of machine-learning for "multi-omics" marine metabolite datasets.
Ausgabe der Zeitschrift
165

Data source: DBLP

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