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).


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

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