HMN 2025: How AI platform uses 3D visualization to reveal disease biomarkers in multiomics data

Researchers say the ability to visualize health data in three dimensions can unlock patterns and relationships among predictive biomarkers that conventional data visualization can’t convey. That’s why Zeeshan Ahmed and his team at Rutgers developed 3D IntelliGenes, an advanced artificial intelligence and machine-learning platform with the ability to visually analyze multiple types of biological and clinical data—known as multiomics.

3D data visualization can aid in the identification of biomarkers to enable earlier diagnosis and personalized treatment options for diseases.

3D IntelliGenes was developed as an accessible software to support scientists from diverse bioinformatic backgrounds. The platform is open source, available across platforms including Windows, macOS and Linux, and optimized for use on standard desktops.

Zeeshan Ahmed is a core member of the Rutgers Institute for Health, Health Care Policy and Aging Research (IFH), an assistant professor of medicine at Robert Wood Johnson Medical School and the lead author of the study highlighting the development of 3D Intelligenes published in BMC Medical Research Methodology.

Ahmed says, “Our goal is for multiomics data to become more accessible to the wider scientific community so researchers can better understand diseases and their treatments. For example, the software could produce visual graphs showing relationships between biomarkers, such as cardiovascular disease indicators, which could help researchers and clinicians analyze and interpret heart health and disease risks more effectively.”