Benchmarking VAE latent features in downstream tasks for cancer related predictions

Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Variational Auto-Encoders (VAEs) are used to assist in this process. VAEs are a type of neural network seeking to encode data into a smaller dimension called latent space. These latent features are then used to do downstream task analysis such as...

Interpretable classification of tumours through multiple instance learning and somatic mutations

Next generation sequencing is brought into the clinic. Screening of disease associated genes will aid the diagnosis of disorders with a genetic component. The diagnosis of cancer is of particular interest due to its variety and prevalence. The obtained mutations provide clues about underlying biological properties that could be used for...