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## About
This is the official website of the ViResNet project. This project is aimed to devlop deep residual neural networks to detect virus infection.
In our work thus far we developed deep learning approach to identify herpesvirus and adenovirus infected cells in the absence of virus specific markers, and at high accuracy. This was possible based on DNA labeling alone. Upon these results we show that by extending the we can predict the lytic fate of adenovirus infected cells - a poorly understood phenomenon with large pathobiological and therapeutic implications.
Furthermore, our approach provided the ability to identify cells that will undergo lysis up to 20 hours before the occurance.
### Deep learning of virus infections reveals mechanics of lytic cells
Vardan Andriasyan*, Artur Yakimovich*, Fanny Georgi, Anthony Petkidis, Robert Witte, Daniel Puntener, and Urs F. Greber
The preprint of the ViResNet paper is available on [BioRxiv DOI:10.1101/798074](https://www.biorxiv.org/content/10.1101/798074v1.article-info). Please cite us!
BIORXIV/2019/798074
## Source code availability
The software will be available on GitHub for forking, cloning and download under GPLv3 open source license from [https://github.com/viresnet/viresnet](https://github.com/viresnet/viresnet).
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Copyright © 2016 - Vardan Andriasyan and Artur Yakimovich