Informing and improving biomedical research
We use mathematical, computational, and AI approaches to inform and improve experimental, pre-clinical, clinical research and drug discovery.
Our Approaches
By combining cutting-edge technology with deep expertise and a passion for positively impacting people's lives, we have been able to push the boundaries of what is possible and make a meaningful difference in the fight against these debilitating diseases and conditions.
- We have used mathematical models, pharmacokinetic and pharmacodynamic (PK/PD) approaches, and quantitative systems pharmacology (QSP) to study the mechanisms and response of patients to drugs.
- We have successfully applied machine learning (ML) approaches to study various biomedical questions, such as predicting the mutability of a pathogen..
- We have demonstrated the potential of integrating mechanistic, and machine learning (ML) approaches to offer a powerful and novel tool for understanding complex biological systems and developing effective drug therapies.
Sample Case Studies
We have a proven track record of success in using these approaches to gain new insights and develop innovative solutions for a wide range of medical conditions, including hydrocephalus, cancer, and infectious diseases.
Deep learning characterization of brain tumours with diffusion-weighted imaging - read more ...
A novel statistical method predicts mutability of the genomic segments of the SARS-CoV-2 virus - read more ...
Systems biology-informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade - read more ...
Our Team
Our team comprises a group of highly skilled and dedicated individuals who possess a wealth of knowledge and experience in applying mathematical and computational methods to understand and combat some of the most pressing medical challenges of our time.