Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Jamshidi, MB; Hoang, DT; Nguyen, DN; Niyato, D; Warkiani, ME

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Jamshidi, MB Hoang, DT Nguyen, DN Niyato, D Warkiani, ME

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Comput Biol Med, 2025, 189, pp. 109970
Issue Date:: 2025-05

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Field	Value	Language
dc.contributor.author	Jamshidi, MB
dc.contributor.author	Hoang, DT
dc.contributor.author	Nguyen, DN
dc.contributor.author	Niyato, D
dc.contributor.author	Warkiani, ME
dc.date.accessioned	2025-08-19T03:08:13Z
dc.date.available	2025-03-01
dc.date.available	2025-08-19T03:08:13Z
dc.date.issued	2025-05
dc.identifier.citation	Comput Biol Med, 2025, 189, pp. 109970
dc.identifier.issn	0010-4825
dc.identifier.issn	1879-0534
dc.identifier.uri	http://hdl.handle.net/10453/189492
dc.description.abstract	Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Comput Biol Med
dc.relation.isbasedon	10.1016/j.compbiomed.2025.109970
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Biomedical Engineering
dc.subject.classification	3102 Bioinformatics and computational biology
dc.subject.classification	4203 Health services and systems
dc.subject.classification	4601 Applied computing
dc.subject.mesh	Humans
dc.subject.mesh	Algorithms
dc.subject.mesh	Bacteria
dc.subject.mesh	Convolutional Neural Networks
dc.subject.mesh	Deep Learning
dc.subject.mesh	Federated Learning
dc.subject.mesh	Internet of Things
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Humans
dc.subject.mesh	Bacteria
dc.subject.mesh	Algorithms
dc.subject.mesh	Deep Learning
dc.subject.mesh	Internet of Things
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Federated Learning
dc.subject.mesh	Convolutional Neural Networks
dc.subject.mesh	Humans
dc.subject.mesh	Algorithms
dc.subject.mesh	Bacteria
dc.subject.mesh	Convolutional Neural Networks
dc.subject.mesh	Deep Learning
dc.subject.mesh	Federated Learning
dc.subject.mesh	Internet of Things
dc.subject.mesh	Neural Networks, Computer
dc.title	Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.
dc.type	Journal Article
utslib.citation.volume	189
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Cyber Security and Privacy (CCSP)
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/Engineering and IT Related HDR Students
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-08-19T03:07:45Z
pubs.publication-status	Published
pubs.volume	189

Abstract:

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries.

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http://hdl.handle.net/10453/189492