A Meharry School of Applied Computational Sciences research team is launching a study to determine if quantum computing can accurately predict the progression of triple-negative breast cancer (TNBC). Dr. Pushpita Chatterjee, assistant professor of computer science and data science, will lead the investigation.
The project, “Classical vs. Quantum ML for Multimodal Predictive Modeling of TNBC Progression,” is supported by a National Institutes of Health (NIH) Research Centers in Minority Institutions (RCMI) pilot grant. The study arrives at a critical time; TNBC remains one of the most aggressive forms of breast cancer, often lacking the specific predictive tools required for early and accurate prognostic modeling.
TNBC research is particularly relevant at Meharry. Extensive evidence shows that TNBC disproportionately affects Black women, who face both a higher incidence of the disease and a significantly higher risk of all‑cause mortality.
The research focuses on developing multimodal predictive models by integrating complex datasets, including electronic health records, diagnostic imaging, and clinical data. The team will conduct a comparative analysis between classical machine learning and emerging quantum machine learning approaches.
“Quantum computing can process information in exciting new ways that traditional computing cannot match,” said Dr. Chatterjee. “By leveraging quantum-enhanced algorithms alongside traditional machine learning, we seek to improve prognostic accuracy and identify novel biomarkers.”
“Our team believes this research can advance the field of precision oncology, potentially leading to more effective and personalized treatment strategies for patients facing a TNBC diagnosis,” she added.
Beyond the laboratory findings, the grant supports the next generation of scientists by funding two research assistants. These positions will provide students with specialized experience in the high-growth fields of bioinformatics and quantum-enhanced algorithms.
