Next Article in Journal
From an Entropic Measure of Time to Laws of Motion
Previous Article in Journal
Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data
Previous Article in Special Issue
Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Entropy 2019, 21(3), 221; https://doi.org/10.3390/e21030221

Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images

1
Department of Computer Science, Bar-Ilan University, Ramat-Gan 5290002, Israel
2
Gonda Brain Research Center, Bar-Ilan University, Ramat-Gan 5290002, Israel
3
Center for Automation Research, UMIACS, University of Maryland at College Park, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 26th International Conference on Artificial Neural Networks (ICANN 2017), Alghero, Italy, 11–14 September, 2017.
Received: 27 March 2018 / Revised: 22 October 2018 / Accepted: 19 December 2018 / Published: 26 February 2019
Full-Text   |   PDF [1439 KB, uploaded 26 February 2019]   |  

Abstract

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. View Full-Text
Keywords: deep learning; convolutional neural networks; denoising autoencoders; ISH images; gene categorization deep learning; convolutional neural networks; denoising autoencoders; ISH images; gene categorization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Cohen, I.; David, E.O.; Netanyahu, N.S. Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images. Entropy 2019, 21, 221.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top