  Despite great advances, molecular cancer pathology is often limited to the
use of a small number of biomarkers rather than the whole transcriptome, partly
due to computational challenges. Here, we introduce a novel architecture of
Deep Neural Networks (DNNs) that is capable of simultaneous inference of
various properties of biological samples, through multi-task and transfer
learning. It encodes the whole transcription profile into a strikingly
low-dimensional latent vector of size 8, and then recovers mRNA and miRNA
expression profiles, tissue and disease type from this vector. This latent
space is significantly better than the original gene expression profiles for
discriminating samples based on their tissue and disease. We employed this
architecture on mRNA transcription profiles of 10787 clinical samples from 34
classes (one healthy and 33 different types of cancer) from 27 tissues. Our
method significantly outperforms prior works and classical machine learning
approaches in predicting tissue-of-origin, normal or disease state and cancer
type of each sample. For tissues with more than one type of cancer, it reaches
99.4\% accuracy in identifying the correct cancer subtype. We also show this
system is very robust against noise and missing values. Collectively, our
results highlight applications of artificial intelligence in molecular cancer
pathology and oncological research. DeePathology is freely available at
\url{https://github.com/SharifBioinf/DeePathology}.
