Fig. 1
From: RareNet: a deep learning model for rare cancer diagnosis
RareNet’s Variational Auto-Encoder (VAE) Architecture. The model’s input has 24,565 nodes and takes in DNA methylation data. The process starts similar to that of CancerNet9. The model processes DNA methylation data through ReLU-activated layers, compressing it into a probabilistic latent space. The data is reconstructed via a sigmoid-activated decoder. From the latent space, latent variables are sampled and classification of rare cancer and normal samples is performed using a softmax layer. Initially, transfer learning from CancerNet is performed, wherein the parameters of the encoder and decoder layers, as obtained from CancerNet, are frozen and only the softmax classifier is trained.
