Dr Musalula Sinkala

Postdoctoral Research Fellow

Affiliations

  1. Research Fellow, Institute of Infectious Disease and Molecular Medicine
  2. Computational Biology Group

Key Expertise

Bioinformatics, Biomarker Discovery, Cancer (Prevalent) Drug Discovery, Drug Resistance, Genomic & Precision Medicine, Molecular Medicine, Non-communicable Disease, Omics, Machine learning and AI

Main Research Focus

Musalula’s work focuses on applying bioinformatics and AI-driven approaches to advance precision medicine and understand complex diseases. He specializes in the integration and analysis of multi-omics data—including genomic, transcriptomic, proteomic, and imaging datasets—using machine learning and big data analytics. His research includes AI-powered disease subtyping, drug response prediction, and biomarker discovery, with contributions that extend to patent applications in AI-based clinical diagnostics and therapeutic target identification. In addition, he is actively involved in international collaborations aimed at advancing computational biology in Africa, particularly in the areas of genomic medicine, drug discovery, and population health research

Most Significant Paper Authored in 2024

Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects.

Sinkala, M., Naran, K., Ramamurthy, D., Mungra, N., Dzobo, K., Martin, D., & Barth, S. (2024).

This study provides a novel integrative framework linking cell surface receptor (CSR) transcript levels to drug responses and off-target effects in breast cancer. By combining machine learning with transcriptomics and clinical data, it reveals that CSR expression profiles in tumours and normal tissues predict therapeutic response and adverse drug reactions. The work identifies CSR biomarkers across subtypes, aiding personalised treatment. Importantly, it facilitates rational drug target selection by correlating CSR expression with drug efficacy and toxicity. This paper advances precision oncology by highlighting the translational potential of transcriptomic profiling in optimising cancer therapy and minimising patient harm.