Precision Medicine Project
Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis
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 visualization. BMC Medical Research Methodology. PMID: 40781583 (BMC, Springer Nature)
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
