Featured Publications
Translational bioinformatics and machine learning framework for biomarker ...
Ahmed, Z., Govindareddy, P., DeGroat, W., Narayanan, R., Peker, E., & Zeeshan, S. (2026).Translational bioinformatics and machine learning framework for biomarker discovery, disease prediction, and patient profiling for precision medicine. Preprint, medRx.
Multi-omics analysis of genetic drivers linking AS & LVDD in Heart Failure
Ahmed, Z., Govindareddy, P., Mathew, M., Yanamala, N., Sengupta, P. (2026). Multi-Omics Analysis of Genetic Drivers Linking Aortic Stenosis and Left Ventricular Diastolic Dysfunction in Heart Failure. BioData Mining. PMID: 42277826. (Springer Nature & BMC).
Artificial intelligence to investigate metabolomics data for precision medicine
Shenouda, A., Senthilkumar, S., Mourad, Y., Xie, J., Peker, E., Saman, Z., & Ahmed, Z. (2026). Artificial intelligence to investigate metabolomics data for precision medicine. Metabolomics. 22, 29. PMID: 41758481. (Springer Nature).
3D IntelliGenes: Real-world and Multidimensional AI/ML Application
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, 25(1), 193. PMID: 40781583. (Springer Nature, BMC).
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
