IntelliGenes, A first-of-its-kind software created at Rutgers Health combines artificial intelligence (AI) and machine learning approaches to measure the significance of specific genomic biomarkers to help predict disease in individuals, according to its developers.
A study published in Bioinformatics explains how IntelliGenes can be used by a wide range of users to analyze polygenomic and clinical data.
Zeeshan Ahmed, lead author of the study and a faculty member at the Rutgers Institute for Health, Health Care Policy and Aging Research (IFH), said there are currently no artificial intelligence or machine learning tools available to explore and interpret the full human genome, especially for non-experts. Ahmed and members of the Rutgers lab designed IntelliGenes so that anyone can use the platform, including students or those who are not well versed in bioinformatics techniques or do not have access to high performance computers.
The software combines conventional statistical methods with cutting-edge machine learning algorithms to produce personalized patient predictions and a visual representation of biomarkers important for disease prediction.
In another study, published in Scientific Reportsthe researchers applied IntelliGenes to discover new biomarkers and predict cardiovascular diseases with high accuracy.
There is enormous potential in the convergence of datasets and the groundbreaking advances in artificial intelligence and machine learning.”
Zeeshan Ahmed, lead study author, assistant professor of medicine, Robert Wood Johnson Medical School
“IntelliGenes can support personalized early detection of common and rare diseases in individuals, as well as pave the way for broader research that ultimately leads to new interventions and treatments.”
The researchers tested the software using Amarel, the high-performance computing cluster managed by the Rutgers Office of Advanced Computing Research. The office provides a computing and data research environment for Rutgers researchers engaged in computationally complex and data-intensive projects.
Study authors include William DeGroat, Dinesh Mendhe, Atharva Bhusari and Habiba Abdelhalim of the IFH and Saman Zeeshan of the Rutgers Cancer Institute of New Jersey.
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Journal Reference:
DeGroat, W., et al. (2023). IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genome profiling. Bioinformatics. doi.org/10.1093/bioinformatics/btad755.
