ECOLE: Learning to call copy number variants on whole exome sequencing data

ECOLE overview

Our model ECOLE is a deep neural network model which uses a variant of the transformer architecture²⁰ at its core. The transformer is a parallelizable encoder-decoder model that receives an input and applies alternating layers of multi-headed self-attention, multi-layer perceptron (MLP), and layer normalization layers to it. Transformer architecture has achieved state-of-the-art results in signal processing over recurrent neural networks in the natural language processing domain²⁰ as well as recently over the convolutional-based models in the computer vision domain²³.

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