  Breast cancer has long been a prominent cause of mortality among women.
Diagnosis, therapy, and prognosis are now possible, thanks to the availability
of RNA sequencing tools capable of recording gene expression data. Molecular
subtyping being closely related to devising clinical strategy and prognosis,
this paper focuses on the use of gene expression data for the classification of
breast cancer into four subtypes, namely, Basal, Her2, LumA, and LumB. In stage
1, we suggested a deep learning-based model that uses an autoencoder to reduce
dimensionality. The size of the feature set is reduced from 20,530 gene
expression values to 500 by using an autoencoder. This encoded representation
is passed to the deep neural network of the second stage for the classification
of patients into four molecular subtypes of breast cancer. By deploying the
combined network of stages 1 and 2, we have been able to attain a mean 10-fold
test accuracy of 0.907 on the TCGA breast cancer dataset. The proposed
framework is fairly robust throughout 10 different runs, as shown by the
boxplot for classification accuracy. Compared to related work reported in the
literature, we have achieved a competitive outcome. In conclusion, the proposed
two-stage deep learning-based model is able to accurately classify four breast
cancer subtypes, highlighting the autoencoder's capacity to deduce the compact
representation and the neural network classifier's ability to correctly label
breast cancer patients.
