  In recent years, large datasets of high-resolution mammalian neural images
have become available, which has prompted active research on the analysis of
gene expression data. Traditional image processing methods are typically
applied for learning functional representations of genes, based on their
expressions in these brain images. In this paper, we describe a novel
end-to-end deep learning-based method for generating compact representations of
in situ hybridization (ISH) images, which are invariant-to-translation. In
contrast to traditional image processing methods, our method relies, instead,
on deep convolutional denoising autoencoders (CDAE) for processing raw pixel
inputs, and generating the desired compact image representations. We provide an
in-depth description of our deep learning-based approach, and present extensive
experimental results, demonstrating that representations extracted by CDAE can
help learn features of functional gene ontology categories for their
classification in a highly accurate manner. Our methods improve the previous
state-of-the-art classification rate (Liscovitch, et al.) from an average AUC
of 0.92 to 0.997, i.e., it achieves 96% reduction in error rate. Furthermore,
the representation vectors generated due to our method are more compact in
comparison to previous state-of-the-art methods, allowing for a more efficient
high-level representation of images. These results are obtained with
significantly downsampled images in comparison to the original high-resolution
ones, further underscoring the robustness of our proposed method.
