Muhammad Nabeel Asim

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.

BIND predicts biological interactions using knowledge graph embeddings and machine learning, achieving high F1 scores and identifying novel drug-phenotype interactions supported by literature.

This paper introduces a framework combining knowledge graphs and LLM agents to automate biomedical discovery by identifying relevant pathways, designing AI predictors, and proposing wet-lab experiments.

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.

NTpred improves the identification of tyrosine nitration sites in protein sequences using advanced feature extraction and ensemble classifiers, achieving better performance than existing methods on benchmark datasets.

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