Mohamed Omar

sctrial provides participant-level differential analysis for longitudinal single-cell experiments, reducing pseudoreplication-driven signal inflation and improving reliability of biological interpretations.

The paper develops mechanistic classifiers using biological constraints to improve the robustness and interpretability of gene signatures in cancer prediction.

This study identifies multi-omics prognostic signatures for prostate cancer using transcriptomics and metabolomics data, showing their ability to stratify patients into distinct risk groups.

The paper identifies differentially expressed genes in post-mortem brain tissues of bipolar disorder patients and validates them across datasets, finding associations with calcium transport, inflammation, and DNA damage response.