EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.

ABSTRACT: Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer t

SUBMITTER: Joshi P 

PROVIDER: S-EPMC9418241 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature