Muhammad Sajjad

Explainable Artificial Intelligence for Cross-Dataset Generalizable Biomarker Discovery in Cardiovascular diseases (CVDs)

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.

Assessment of the Genetic Architecture at Early-Stage Drought Tolerance in Wheat Using UAV-Based Multispectral Imaging

This study uses UAV-based multispectral imaging to assess drought tolerance in wheat, identifying candidate genes and QTNs associated with early-stage drought stress.

Artificial Intelligence Powered Biomarker Discovery: A Large-Scale Analysis of 236 Studies Across 19 Therapeutic Areas and 147 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.

FROM CANCER MOLECULAR SUBTYPE TO AI HYPE: BENCHMARKING AI IN CANCER MOLECULAR SUBTYPING

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.

Pyruvate dehydrogenase dependent metabolic program affects oligodendrocyte maturation and remyelination

This study shows that pyruvate dehydrogenase (Pdh) activity is critical for oligodendrocyte maturation and remyelination by regulating mitochondrial respiration and Olig1 acetylation.