Featured Publications

Multimodal AI/ML for discovering novel biomarkers and predicting disease ...

Degroat, W., Abdelhalim, H., Peker, E., Sheth, N., Narayanan, R., Zeeshan, S., Liang, B.T., & Ahmed, Z. (2024). Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases. Scientific Reports. 14, 26503. PMID: 39489837. (Nature)

3D IntelliGenes: AI/ML application multi-dimensional visualization

Narayanan, R., Peker, E., Degroat, W., Mendhe, D., Zeeshan, S., & Ahmed, Z. (2025). 3D IntelliGenes: AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualizationBMC Medical Research Methodology.  PMID: 40781583 (BMC, Springer Nature)

Omics approaches to understand cardiovascular disease

Ahmed, Z., and Jun, G. (2025). Omics approaches to understand cardiovascular disease. BMC Cardiovascular Disorders. 25: 869. PMID: 41413447. (Springer Nature, BMC).

Artificial intelligence and precision medicine

Ahmed, Z., and Alam MN. (2026). Artificial intelligence and precision medicine. Scientific Reports. 16, 2168. PMID: 41547658. (Nature).

AHMED LAB

We are a highly collaborative and productive scientific lab driven towards the implementation of Artificial Intelligence (AI), Machine Learning (ML), and bioinformatics and biomedical informatics applications to support translational research and precision medicine. More specifically, we implement innovative multi-modal AI/ML approaches to discover novel biomarkers and predict complex, known, and rare diseases.

In recent years, our research has been centered on examining cardiovascular diseases and related genes through whole genome/exome sequencing (WGS/WES) and RNA-seq, alongside demographic, environmental, and clinical data. We have employed bioinformatics techniques to analyze variant and expression patterns associated with disease phenotypes, harnessing AI/ML for precise disease susceptibility predictions.

We are the first to produce a peer reviewed, customizable, multi-modal, and user-friendly AI/ML pipeline i.e., IntelliGenes, for biomarker discovery and predic

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