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Related Experiment Video

Updated: Dec 28, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Image processing techniques represent innovative tools for comparative analysis of proteins.

Reza Ahsan¹, Mansour Ebrahimi²

¹Department of Information Technology, School of Engineering, University of Qom, Qom, Iran.

Computers in Biology and Medicine

|February 20, 2020

Summary

A new Image Processing Techniques and Convolutional Deep Neural Network (IPT-CNN) approach accurately classifies Influenza A virus (IAV) subtypes using protein data. This method offers a faster, reliable way to analyze large protein datasets for subtype prediction.

Keywords:

Convolutional deep neural network Image processing techniques Micro-organism Protein

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Published on: April 8, 2016

Consensus Brain-derived Protein, Extraction Protocol for the Study of Human and Murine Brain Proteome Using Both 2D-DIGE and Mini 2DE Immunoblotting

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Published on: April 10, 2014

Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy

Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy

Published on: November 20, 2018

Area of Science:

Bioinformatics
Computational Biology
Virology

Background:

Traditional bioinformatic and data-mining methods are used for protein analysis.
Comparative analysis of large protein datasets presents challenges.

Purpose of the Study:

To introduce a novel, robust, and reliable approach for comparative protein analysis.
To demonstrate the efficacy of the Image Processing Techniques and Convolutional Deep Neural Network (IPT-CNN) for predicting Influenza A virus (IAV) subtypes.

Main Methods:

Combined Image Processing Techniques (IPT) with Convolutional Deep Neural Network (CNN).
Utilized over 8000 sequences of haemagglutinin (HA) and neuraminidase (NA) surface proteins from IAV subtypes.
Converted protein sequence datasets into binary images for analysis.

Main Results:

Achieved 100% accuracy in classifying IAV subtypes.
Demonstrated a significantly shorter analysis time compared to non-image-based approaches.
Validated the IPT-CNN approach for proteome-based classification.

Conclusions:

The IPT-CNN approach provides a powerful tool for the comparative analysis and classification of large protein datasets.
This method shows potential for classifying other protein types beyond viral subtypes.
The study highlights the effectiveness of image-based deep learning in proteomics.