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IntelliGenes accessible AI software helps predict diseases

Jan. 24, 2024.
2 min. read 8 Interactions

Cutting-edge AI and machine-learning algorithms can produce personalized patient predictions

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Amara Angelica

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Amara Angelica is Senior Editor, Mindplex

(credit: A. Angelica, DALL-E 3)

To help predict diseases, researchers at Rutgers Health have developed IntelliGenes software, which combines artificial intelligence (AI) and machine-learning approaches.

A study published in Bioinformatics explains how IntelliGenes can be used by a wide range of users to analyze multigenomic and clinical data. It’s accessible by anyone, says Zeeshan Ahmed, lead author of the study and a faculty member at Rutgers Institute for Health, Health Care Policy and Aging Research (IFH).

Personalized patient predictions

Previously, there were no AI or machine-learning tools available to investigate and interpret the complete human genome, especially for non-experts. So Ahmed and members of his Rutgers lab developed IntelliGenes software. It combines conventional statistical methods with cutting-edge machine-learning algorithms to produce personalized patient predictions and a visual representation of the biomarkers significant to disease prediction.

In another study, published in Scientific Reports, the researchers applied IntelliGenes to discover novel biomarkers and predict cardiovascular disease with high accuracy.

“There is huge potential in the convergence of datasets and the staggering developments in artificial intelligence and machine learning,” said Ahmed, who is also an assistant professor of medicine at Robert Wood Johnson Medical School.

Early detection of common and rare diseases

IntelliGenes can support personalized early detection of common and rare diseases in individuals, as well as open avenues for broader research ultimately leading to new interventions and treatments,” said Ahmed.

The researchers tested the software using Amarel, a high-performance computing cluster managed by the Rutgers Office of Advanced Research Computing.

Citation: DeGroat, W., Mendhe, D., Bhusari, A., Abdelhalim, H., Zeeshan, S., & Ahmed, Z. (2023). IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics, 39(12).

Citation: DeGroat, W., Abdelhalim, H., Patel, K. et al. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci Rep 14, 1 (2024).

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