About Us
Advancing biomedical research through better statistical modelling
Saddle Point Science develops novel scientifically validated statistical methods and software to generate reliable insights from complex biomedical data.
We help researchers in healthcare organisations and life sciences companies address challenging data analysis problems, particularly those that emerge when datasets are high-dimensional, heterogeneous, or limited in size.
Our Mission
Making complex biomedical data more informative
To advance biomedical research through innovative mathematical, statistical and computational methods that enable more precise prediction of treatment response and clinical risk.
What we do
From complex data to actionable research insights
​Our work combines advanced statistical methodology, scientific research and software development to address complex biomedical research questions.
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Risk & Treatment Response
Generate personalised risk and treatment response predictions from complex biomedical data. -
​Patient Subgroup Discovery
Identify and understand latent patient subgroups and uncover meaningful differences within patient populations. -
​Federated Analysis
Enable collaborative statistical analysis across distributed datasets while keeping data at the contributing organisations.
Our story
Saddle Point Science began as a spin-out from King's College London, driven by the recognition that increasingly complex biomedical data require better, more modern analytical methods. We saw the need for approaches that can uncover meaningful patterns in high-dimensional datasets and support more reliable scientific insights. Building on this foundation, we continued our journey in the Netherlands, alongside the United Kingdom, where we are developing innovative statistical solutions to help researchers and organisations unlock the full potential of their data.
Ready to explore what your data can reveal?
Do you have a challenging biomedical dataset or research question?
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Whether you are working with a small dataset, high-dimensional measurements, heterogeneous patient populations or distributed clinical data, our team can help explore whether our approach is suitable for your research questions.