Wide and deep learning for automatic cell type identification.

ABSTRACT: Cell type classification is an important problem in cancer research, especially with the advent of single cell technologies. Correctly identifying cells within the tumor microenvironment can provide oncologists with a snapshot of how a patient's immune system reacts to the tumor. Wide and deep learning (WDL) is an approach to construct a cell-classification prediction model that can learn patterns within high-dimensional data (deep) and ensure that biologically relevant features (wide) remain in the final model. In this paper, we demonstrate that regularization can prevent overfitting and adding a wide component to a neural network can result in a model with better predictive performance. In particular, we observed that a combination of dropout and ℓ 2 regularization can lead to a val

SUBMITTER: Wilson CM 

PROVIDER: S-EPMC7878986 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature