LQ placeholderMathematics for personalized medicine

Mathematics for personalized medicine

Creating powerful mathematical methods for predicting cancer outcomes that can be coded in algorithms for fast parallel processing.

Background Modern cancer medicine can access vast amounts of patient-specific data, such as individual genomes, blood bio-markers and medical images. But it fails to use this information effectively. Analysing next-generation data with previous-generation tools causes avoidable patient harm and misses clues to new treatments. It also contributes to the high failure rates of clinical trials.

Project Most mathematical and machine learning methods for predicting cancer outcomes have a common Achilles heel. When the amount of information becomes too large, they confuse noise with real patterns and miss the wood for the trees. This breakdown is known as overfitting. We combat it by creating more powerful mathematical methods based on treating topological patterns in data as constraints on a random graph ensemble. These can be coded in algorithms that let multiple computers work in parallel.

Consequences More accurate clinical prediction reduces the likelihood of harmful, ineffective treatments and saves lives. Each doomed trial that gets stopped early by better analysis of preliminary data saves over £100 m for the public health services and the drug industry. And rescuing failing trials by identifying appropriate drug recipients broadens the range of available drugs.

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