Unsupervised generative and graph representation learning for modelling cell differentiation.

ABSTRACT: Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological

SUBMITTER: Bica I 

PROVIDER: S-EPMC7300092 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature