Innovative AI Platform Employs 3D Visualization to Identify Disease Biomarkers in Multiomics Data
A new AI platform called 3D IntelliGenes leverages 3D visualization to uncover disease biomarkers in complex multiomics data, advancing early diagnosis and personalized medicine.
Researchers at Rutgers University-New Brunswick have developed a groundbreaking AI-driven platform called 3D IntelliGenes, which employs advanced 3D visualization techniques to analyze multiomics data—comprising various biological and clinical information. This innovative approach allows scientists to uncover intricate patterns and relationships among disease biomarkers that traditional 2D data analysis methods might overlook. By visualizing complex datasets in three dimensions, the platform enhances the identification of early diagnostic markers and personalized treatment targets for various diseases, including cardiovascular conditions.
3D data visualization is increasingly recognized for its potential to improve biomarker discovery and disease understanding. Recognizing that accessible tools are vital for a broader scientific community, the development team designed 3D IntelliGenes as open-source software compatible across multiple operating systems such as Windows, macOS, and Linux, optimized for standard desktop use.
Zeeshan Ahmed, a key researcher involved in the project and an assistant professor at Robert Wood Johnson Medical School, emphasized the goal of making multiomics data more approachable. He stated that the platform could generate visual graphs illustrating relationships among biomarkers, thereby assisting researchers and clinicians in better analyzing and interpreting data related to disease risks and health indicators. Notably, the software has the potential to support early diagnosis and personalized treatment strategies, especially for complex conditions like heart disease.
The study describing 3D IntelliGenes was published in BMC Medical Research Methodology (2025) and highlights the tool's capacity to facilitate deeper insights into biological data through multi-dimensional visualization. This advancement marks a significant step towards integrating sophisticated AI and visualization techniques into routine biomedical research, fostering a more comprehensive understanding of disease mechanisms.
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