3D IntelliGenes: AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization.

Journal: BMC medical research methodology
Published Date: Aug 8, 2025

Abstract

BACKGROUND: The cutting-edge artificial intelligence (AI) and machine learning (ML) techniques have proven effective at uncovering elucidative knowledge on disease-causing biomarkers and the biological underpinnings of a plethora of human diseases. However, the high-dimensional nature of multi-omics data presents numerous challenges in its effective presentation, annotation, and interpretation. Traditional 2D visualizations often fall short in capturing the intricate relationships between multi-omics features, hindering our ability to identify meaningful correlations.

Authors

  • Rishabh Narayanan
    Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson St, New Brunswick, NJ, 08901, USA.
  • Elizabeth Peker
    Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson St, New Brunswick, NJ, 08901, USA.
  • William DeGroat
    Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA.
  • Dinesh Mendhe
    Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA.
  • Saman Zeeshan
    The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Zeeshan Ahmed
    The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA zeeshan.ahmed@jax.org.

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