Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.

ABSTRACT: Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cel

SUBMITTER: Abdolhosseini F 

PROVIDER: S-EPMC6382891 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature