Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases.

ABSTRACT: Gene expression data is commonly used at the intersection of cancer research and machine learning for better understanding of the molecular status of tumour tissue. Deep learning predictive models have been employed for gene expression data due to their ability to scale and remove the need for manual feature engineering. However, gene expression data is often very high dimensional, noisy, and presented with a low number of samples. This poses significant problems for learning algorithms: models often overfit, learn noise, and struggle to capture biologically relevant information. In this article we utilise external biological knowledge embedded within structures of gene interaction graphs such as protein-protein interaction networks (PPI) to guide the construction of predictive models. We

SUBMITTER: Scherer P 

PROVIDER: S-EPMC8826027 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature