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In-situ Hybridization

In-situ Hybridization

In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
Types of probes and labels
A probe is a complementary strand of DNA or RNA that binds to corresponding nucleotide sequences in a cell. Many...

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Updated: Nov 27, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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

Ido Cohen¹, Eli Omid David¹, Nathan S Netanyahu^1,2,3

¹Department of Computer Science, Bar-Ilan University, Ramat-Gan 5290002, Israel.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

A new deep learning method using convolutional denoising autoencoders (CDAE) generates compact gene expression image representations. This approach significantly improves gene ontology classification accuracy and efficiency.

Keywords:

ISH images convolutional neural networks deep learning denoising autoencoders gene categorization

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Published on: March 3, 2023

Area of Science:

Neuroscience
Bioinformatics
Computational Biology

Background:

Large datasets of mammalian neural images are available for gene expression analysis.
Traditional methods use image processing for gene expression representation, but have limitations.

Purpose of the Study:

To develop a novel deep learning method for generating compact, translation-invariant representations of in situ hybridization (ISH) images.
To improve the accuracy and efficiency of functional gene category classification.

Main Methods:

Utilized end-to-end deep learning with convolutional denoising autoencoders (CDAE) for raw pixel processing.
Generated compact image representations invariant to translation.

Main Results:

Achieved a highly accurate classification rate for functional gene ontology categories, improving AUC from 0.92 to 0.997 (96% error rate reduction).
Generated more compact representation vectors compared to previous state-of-the-art methods.
Demonstrated robustness with significantly downsampled images.

Conclusions:

The CDAE-based method offers a robust and efficient approach for analyzing gene expression data from neural images.
This deep learning strategy enhances the understanding of gene function and classification in neuroscience research.