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Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder.

A R Junejo¹, Mohammed K A Kaabar^2,3,4, Xiang Li¹

¹School of Control Science and Control Engineering, Harbin Institute of Technology, Harbin 150001, China.

Computational and Mathematical Methods in Medicine

|December 23, 2021

Summary

New molecular communication networks (MCNs) using deep learning autoencoders (DL-AEs) offer promising antiviral therapies. These systems enable targeted drug delivery and remote health monitoring for emerging infectious diseases and cardiovascular disease.

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Area of Science:

* Computational biology and bioinformatics
* Biomedical engineering
* Infectious disease research

Background:

* Emerging infectious diseases necessitate proactive development of broad-spectrum and host-factor therapies.
* Viral infections can directly and indirectly cause cardiovascular disease (CVD).
* Deep learning autoencoders (DL-AEs) show potential for disease diagnosis and prognosis.

Purpose of the Study:

* To explore molecular communication networks (MCNs) with DL-AEs for novel antiviral therapeutics.
* To design and implement a drug source and target system for MCNs.
* To investigate MCNs for targeted drug delivery (TDD) and remote health monitoring.

Main Methods:

* Utilizing deep learning autoencoder (DL-AE) approach for MCN design.
* Implementing a new drug source and target for MCN under white Gaussian noise.
* Employing biomicrodevices for real-time health monitoring and cloud data storage.

Main Results:

* Simulation results demonstrate reduced bit error rates in transceiver executions for MCNs.
* The study presents a framework for molecular diagnosis, including heart sound classification.
* A biohealth interface for internal and external human body mechanisms is proposed.

Conclusions:

* MCNs integrated with DL-AEs represent a novel therapeutic strategy for infectious diseases and associated conditions like CVD.
* The developed MCN framework supports targeted drug delivery and remote patient monitoring.
* Future healthcare systems can benefit from these advanced bio-integrated communication mechanisms.