A new study shows that machine learning techniques can offer powerful new tools for advancing personalized medicine. The research tackles long-unsolvable problems in biological systems at the cellular level. The new research is one of the first examples of using machine learning to address issues with modeling nonlinear systems and understanding complex processes that might occur in human tissues.
In modeling cellular activity within an organ, it is not possible to individually model each cell in that organ — a cubic centimeter of tissue may contain a billion cells — so researchers rely on what’s known as upscaling. Upscaling seeks to decrease the data required to analyze or model a particular biological process while maintaining the fidelity — the degree to which a model accurately reproduces something — of the core biology, chemistry, and physics occurring at the cellular level.
By reducing the information load for a very complicated system at the cellular level, researchers can better analyze and model the impact or response of those cells with high fidelity without having to model each individual one. Wood describes it as “simplifying a computational problem that has tens of millions of data points by reducing it to thousands of data points.”
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