Featured Publications

IntelliGenes: Artificial Intelligence and Machine Learning Pipeline

Degroat, W., Mendhe, D., Bhurasi, A., Abdelhalim, H., Saman, Z., & Ahmed, Z. (2023). IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics. 39, 12. btad755. PMID: 38096588. (Oxford).

Discovering Biomarkers and Predicting CVDs with 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).

Variant Analysis of Genes Associated with Heart Failure and other 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).

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

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