Ben S. Sidders

This study identifies immunogenomic drivers of acquired resistance to immunotherapy across multiple cancer types using a large-scale clinicogenomic dataset.

PAIRWISE predicts effective personalized drug combinations in cancer using deep learning and transcriptome data, achieving high AUROC scores in validation tests.

The paper introduces a network biology-driven simulation method to predict effective drug combinations and identify biomarkers for DNA Damage Response inhibitors using cancer cell avatars.

The paper evaluates the feasibility of using TMT-iBAQ to identify tumor-enriched and highly expressed cell surface antigens for cancer therapeutics.

The paper introduces OncMTR, a metric identifying genic regions intolerant to germline variation but enriched for cancer-driving mutations, and shows it improves genetic signal detection for hematologic malignancies.

This study integrates drug sensitivity data with CRISPR screens to identify drug mechanisms and biomarkers for cancer therapy.