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Machine learning gives research a leg up

Memphis, Tennessee, January 18, 2021

Researcher smiling at camera.

Xiang Chen, PhD, of Computational Biology, helped develop a machine learning approach to improve epigenetic research.

St. Jude scientists have added deep learning to their cancer research toolkit. Deep learning is part of a family of machine learning methods that use artificial neural networks. It boosts the computational side of science.

MethylationToActivity (M2A) is a machine learning approach for epigenetic research. Created at St. Jude, M2A allows researchers to infer more from a single type of test.

“M2A is a method for integrating DNA methylation information to make it easier to interpret,” said Xiang Chen, PhD, of Computational Biology. “Using M2A, we can learn about promoter activity changes and gene expression changes by DNA methylation in different types of tumors.”

Scientists around the world can use M2A through St. Jude Cloud. The analysis is fast, cheap and reliable.

Genome Biology published this work.

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