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Advance computational tools for multiomics data learning.

Sheikh Mansoor1, Saira Hamid2, Thai Thanh Tuan3

  • 1Department of Plant Resources and Environment, Jeju National University, 63243, Republic of Korea.

Biotechnology Advances
|September 9, 2024
PubMed
Summary
This summary is machine-generated.

Bioinformatics computational tools are essential for analyzing complex omics data. This review details methods for processing, integrating, and analyzing multi-omics data to advance biomedical and plant science research.

Keywords:
BioinformaticsBiomedical scienceComputational toolsMultiomics dataPlant systemPredictive analytics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Omics data is heterogeneous and high-dimensional, requiring specialized computational tools.
  • Multi-omics data is crucial for predictive analytics in big data research.
  • Biomedical and plant science heavily rely on advanced omics data analysis.

Purpose of the Study:

  • To review diverse computational approaches for omics data analysis.
  • To highlight methods for processing, normalizing, integrating, and analyzing multi-omics data.
  • To underscore the importance of computational tools in omics research.

Main Methods:

  • The review discusses various computational methods including similarity-based, network-based, correlation-based, Bayesian, fusion-based, and multivariate techniques.
  • These methods are crucial for handling the complexities of multi-omics data.
  • The focus is on the application of these tools in bioinformatics.

Main Results:

  • A comprehensive overview of computational methodologies for omics data is presented.
  • The unique functionalities of different methods for multi-omics data integration and analysis are detailed.
  • The review emphasizes the critical role of these tools in scientific discovery.

Conclusions:

  • Computational tools are indispensable for modern omics data analysis.
  • Sophisticated methodologies are necessary to unlock the potential of multi-omics data.
  • These tools significantly advance understanding and impact research outcomes in life sciences.