A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data using a Deep Autoencoder and Neural Network

A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data
using a Deep Autoencoder and Neural Network

With the increased affordability and availability of whole-genome sequencing, large-scale and
high-throughput gene expression is widely used to characterize diseases, including cancers.
However, establishing specificity in cancer diagnosis using gene expression data continues to pose
challenges due to the high dimensionality and complexity of the data. Here we present models of
deep learning (DL) and apply them to gene expression data for the diagnosis and categorization of
cancer. In this study, we have developed two DL models using messenger ribonucleic acid
(mRNA) datasets available from the Genomic Data Commons repository. Our models achieved
98% accuracy in cancer detection, with false negative and false positive rates below 1.7%. In our
results, we demonstrated that 18 out of 32 cancer-typing classifications achieved more than 90%
accuracy. Due to the limitation of a small sample size (less than 50 observations), certain cancers
could not achieve a higher accuracy in typing classification, but still achieved high accuracy for
the cancer detection task. To validate our models, we compared them with traditional statistical
models. The main advantage of our models over traditional cancer detection is the ability to use
data from various cancer types to automatically form features to enhance the detection and
diagnosis of a specific cancer type.