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)

IntelliGenes: Interactive and user-friendly multimodal AI/ML application

Narayanan, R., Degroat, W., Mendhe, D., Abdelhalim, H., & Ahmed, Z.(2024). IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. Biology Methods & Protocols. 9(1), bpae040. PMID: 38884000. (Oxford).

VAREANT: a bioinformatics application for gene variant reduction and annotation

Narayanan, R., Degroat, W., Peker, E., Zeeshan, S., & Ahmed, Z. (2025). VAREANT: a bioinformatics application for gene variant reduction and annotation. Bioinformatics Advances. 5, 1. PMID: 39927292. (Oxford).

Applying artificial intelligence & machine learning for analyzing gene expression ...

Ahmed, Z. (2025). Applying AI/ML for analyzing gene expression patterns. Gene Expression Analysis. Methods in Molecular Biology, vol 2880. Editors: Nalini Raghavachari and Natalia Garcia-Reyero. Humana, New York, NY. ISBN: 978-1-0716-4276-4. PMID: 39900767. (Springer 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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