Advance computational tools for multiomics data learning

Sheikh Mansoor

doi:10.1016/j.biotechadv.2024.108447

Advance computational tools for multiomics data learning

2024-12-01

2

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AI

DOI：10.1016/j.biotechadv.2024.108447

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The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusionbased methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Keyword:

Multiomics data

Computational tools

Bioinformatics

Biomedical science

Plant system

Predictive analytics

Advance computational tools for multiomics data learning

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