Article

<i>In silico</i> learning of tumor evolution through mutational time series

2019-03-13

Abstract excerpt

Cancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ Long Short-Term Memory networks (LSTMs), a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capa...

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Literature Corpus work: 905773b2-8a80-5cf9-a87e-cfe54f5b1c9a
DOI: 10.1101/577171

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