Featured Publications

Predicting CVD with AI/ML

Venkat, V., Abdelhalim, H., Degroat, W., Saman. Z., & Ahmed, Z. (2023). Implementing machine learning techniques at RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. Genomics. 115, 2. PMID: 36813091. (Elsevier)

AI/ML in Genomics

Vadapalli, S., Abdelhalim, H., Zeeshan, S., & Ahmed, Z. (2022). Artificial intelligence & machine learning approaches using gene expression and variant data for personalized medicineBriefings in Bioinformatics. 23(5), bbac191. PMID: 35595537. (Oxford)

Investigating AF/CVD Genes

Berber, A., Abdelhalim, H., Zeeshan, S., Vadapalli, S., von-Oehsen, B., Yanamala, N., Sengupta, P., & Ahmed, Z. (2022). RNA-seq driven expression analysis to investigate Cardiovascular disease genes with associated phenotypes among Atrial Fibrillation patients. Clinical and Translational Medicine. PMID: 35875838. (Wiley)

Gene-Disease Annotation

Wable, R., Nair, A.S., Pappu, A., Pierre-Louis, W., Abdelhalim, H., Patel, K., Mendhe, D., Bolla, S., Mittal, S., & Ahmed, Z*. (2023). Integrated ACMG approved genes and ICD codes for the translational research and precision medicine. Database: The Journal of Biological Databases and Curation. 2023, baad033. PMID: 37195695. (Oxford).


The goal of the Ahmed Lab is to build intelligent health systems that systematically incorporate clinical, multi-omics, and phenotypic data into healthcare for providing personalized treatment and better life. We are a computational lab, driven towards the development of bioinformatics tools and methods for multi-omics and clinical data management, processing, integration, annotation, interpretation, and Artificial Intelligence/Machine Learning (AI/ML) ready data generation and sharing. Our focus is on implementing AI/M approaches to the whole genome and transcriptome data for the identification of patterns revealing predictive biomarkers and risk factors to support earlier diagnosis of patients with complex traits, including Cardiovascular, COVID-19, and Alzheimer’s diseases.

We have already designed, developed, and practiced many bioinformatics tools, genomics pipelines, AI/ML algorithms, annotation databases, mobile health platforms, high-performance computing (HPC) based frameworks for multivariate clinical and multi-omics data analysis and dissemination. Furthermore, we have implemented dynamic HIPAA-compliant infrastructures to support various scientific st

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