  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.

Machine Learning;Machine Learning