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OMICS data provides a comprehensive view of biological systems by analysing molecular layers across multiple fields, such as genomics, transcriptomics, proteomics, and metabolomics, each focusing on distinct aspects like gene sequences, RNA transcripts, proteins, and small-molecule metabolites. However, challenges such as high dimensionality, sparsity, noise, and heterogeneity complicate its analysis. Integrating machine learning (ML) and deep learning (DL) techniques has transformed OMICS research, enabling the extraction of meaningful patterns from complex datasets and addressing these challenges. This paper presents a structured overview of OMICS data, reviews ML/DL models applied to critical research tasks, and highlights publicly available datasets. By leveraging advanced computational approaches, it explores the transformative potential of ML/DL in personalized medicine, genetic disease research, and therapeutic innovation.

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Machine Learning and Deep Learning Techniques on OMICS Data

  • 会议论文

  • 发表日期：

    2026年

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  • Anamika Guha,
  • Saptarsi Goswami

摘要

OMICS data provides a comprehensive view of biological systems by analysing molecular layers across multiple fields, such as genomics, transcriptomics, proteomics, and metabolomics, each focusing on distinct aspects like gene sequences, RNA transcripts, proteins, and small-molecule metabolites. However, challenges such as high dimensionality, sparsity, noise, and heterogeneity complicate its analysis. Integrating machine learning (ML) and deep learning (DL) techniques has transformed OMICS research, enabling the extraction of meaningful patterns from complex datasets and addressing these challenges. This paper presents a structured overview of OMICS data, reviews ML/DL models applied to critical research tasks, and highlights publicly available datasets. By leveraging advanced computational approaches, it explores the transformative potential of ML/DL in personalized medicine, genetic disease research, and therapeutic innovation.