Ahtisham Fazeel Abbasi

This study harmonizes cross-platform gene expression data using three techniques, evaluates ML/DL classifiers, and applies XAI methods to identify interpretable biomarkers for cardiovascular diseases.

This study analyzes 236 AI-driven biomarker discovery studies across 147 diseases and 19 therapeutic areas to identify trends and gaps in datasets, methods, and disease-specific biomarkers.

This study benchmarks 35 AI classifiers across 153 cancer datasets to evaluate their performance in molecular subtype classification, identifying optimal data configurations and model types for different omics modalities and cancer types.

The paper introduces a computational predictor, iLEC-DNA, for identifying leccDNA using sequence-derived features, outperforming existing methods across multiple species.

This paper introduces two deep learning predictors for YY1-mediated chromatin loop prediction, showing improved performance across cell lines and benchmark datasets.