Precision Medicine Project
Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis
Predicting Cardiovascular Disease
Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease
Genomic Approaches to Investigate CVD Genes
Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility.
Hygieia: AI/ML pipeline
Hygieia: AI/ML pipeline integrating healthcare and genomics data to investigate genes associated with targeted disorders and predict disease.
Gene-Disease Annotation
Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
Featured Publications
Variant Analysis of HF/CVDs
Mhatre, I., Abdelhalim H, Degroat, W., Ashok, S., Liang, B., & Ahmed, Z. (2023). Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases. Scientific Reports. 13(1), 16769. PMID: 37798313. (Nature).
Investigating AF & HF Genes
Patel, K., Venkatesan, C., Abdelhalim, H., Saman, Z., Arima, Y., Linna-Kuosmanen, S., & Ahmed, Z. (2023). Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. Human Genomics. 17, 47. PMID: 37270590. (BMC)
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 medicine. Briefings in Bioinformatics. 23(5), bbac191. PMID: 35595537. (Oxford)
AHMED LAB
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
