Precision Medicine Project
Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis
Featured Publications
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).
Discovering biomarkers and predicting CVDs with a novel AI/ML approach
Degroat, W., Abdelhalim, H., Patel, K., Mendhe, D., Zeeshan. S., & Ahmed, Z. (2024). Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Scientific Reports. 14, 1. PMID: 38167627. (Nature).
Deciphering genomic signatures associating DOC diseases with CVDs
Ahmed, Z., Degroat, W., Abdelhalim, H., Saman, Z., & Fine, D. (2024). Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches. Clinical Oral Investigations. 52 (28), 1436-3771. PMID: 38163819. (Springer Nature).
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)
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
