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With the increased affordability and availability of whole-genome sequencing,large-scale and high-throughput gene expression is widely used to characterizediseases, including cancers. However, establishing specificity in cancerdiagnosis using gene expression data continues to pose challenges due to thehigh dimensionality and complexity of the data. Here we present models of deeplearning (DL) and apply them to gene expression data for the diagnosis andcategorization of cancer. In this study, we have developed two DL models usingmessenger ribonucleic acid (mRNA) datasets available from the Genomic DataCommons repository. Our models achieved 98% accuracy in cancer detection, withfalse negative and false positive rates below 1.7%. In our results, wedemonstrated that 18 out of 32 cancer-typing classifications achieved more than90% accuracy. Due to the limitation of a small sample size (less than 50observations), certain cancers could not achieve a higher accuracy in typingclassification, 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 theability to use data from various cancer types to automatically form features toenhance the detection and diagnosis of a specific cancer type.

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