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Bioengineering
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Artificial Intelligence in Biotechnology
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Artificial Intelligence in Biotechnology

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 3833

Editors

Dr. Mohammad (Behdad) Jamshidi

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: AI; digital biology; digital twins; decentralized learning; IoBNT
Dr. Omid Moztarzadeh

E-Mail Website
Guest Editor
Faculty of Medicine in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
Interests: biotechnological intelligence; medical AI; cancer diagnostics; predictive healthcare

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is revolutionizing biotechnology, facilitating intelligent, data-driven approaches to analyze biological complexity, enhance diagnostics, and optimize bioprocesses. Advances in machine learning, deep learning, and intelligent systems can be utilized for numerous tasks, ranging from multi-omics integration to real-time biosensor analysis and personalized healthcare solutions. Notably, AI has shown great promise in accelerating cancer diagnostics, prognosis prediction, and therapeutic response modeling.

This Special Issue, “Artificial Intelligence in Biotechnology”, aims to compile original research papers and comprehensive reviews that explore the integration of AI across various biotechnological domains. Topics of interest include, but are not limited to, the following:

  1. AI-driven optimization in bioprocess and fermentation engineering;
  2. Machine learning for genomics, transcriptomics, and proteomics integration;
  3. Deep learning applications in cancer diagnostics and imaging;
  4. Digital twins of biological systems for predictive modeling;
  5. Internet of Bio-Nano Things (IoBNT) and embedded AI for biosensing;
  6. AI in synthetic biology and genetic circuit design;
  7. Federated learning and decentralized AI for biomedical applications;
  8. AI-guided drug discovery and bio-manufacturing.

Contributions on all research areas are welcome, provided that artificial intelligence is central to the methodology or innovation.

Dr. Mohammad (Behdad) Jamshidi
Dr. Omid Moztarzadeh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • biotechnology
  • machine learning
  • deep learning
  • digital twins
  • multi-omics integration
  • cancer diagnostics
  • synthetic biology
  • internet of bio-nano things (IoBNT)
  • drug discovery

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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19 pages, 2692 KB  
Article
A Network-Medicine Framework for Intra-Oral Comorbidity: Age-Stratified Clustering and Quasi-Causal Progression Modeling from Outpatient Electronic Health Records
by Wei Chen, Peng Huang, Zijian Cheng, Yaowu Chen, Xiang Tian, Yumeng Song, Xiaoyan Chen, Qianming Chen and Rui Zhang
Bioengineering 2026, 13(7), 761; https://doi.org/10.3390/bioengineering13070761 (registering DOI) - 29 Jun 2026
Abstract
Background: Network medicine has reshaped how systemic comorbidities are quantified, but the internal comorbidity structure of oral diseases remains undescribed at four-character ICD-10 granularity. Methods: A total of 2,863,671 outpatient visit records from 583,614 patients (2011–2025) were analyzed. Using ICD-10 four-character codes (75 [...] Read more.
Background: Network medicine has reshaped how systemic comorbidities are quantified, but the internal comorbidity structure of oral diseases remains undescribed at four-character ICD-10 granularity. Methods: A total of 2,863,671 outpatient visit records from 583,614 patients (2011–2025) were analyzed. Using ICD-10 four-character codes (75 disease nodes), comorbidity networks were constructed for five age strata, with edges selected by relative risk (RR) > 1.5 and Bonferroni-corrected Fisher’s exact tests. Patient-level longitudinal sequences were mined for progression trajectories, and quasi-causal analyses—Cox regression, negative outcome controls, and Baron–Kenny mediation—were used to evaluate pathway directionality and specificity. Results: The all-age network contained 75 nodes and 167 edges (modularity = 0.53), forming eight communities. Network complexity peaked at 18–29 years and declined with age. Dental caries emerged as the strongest hub in the 60+ stratum (degree = 9). Cox regression adjusted for age, sex, and healthcare utilization confirmed pathway directionality (pulpitis → tooth defect: hazard ratio (HR) = 2.65; caries → pulpitis: HR = 2.25), and negative outcome controls confirmed biological specificity. Mediation analysis showed that pulpitis completely mediated the caries → tooth defect association (proportion mediated ≈ 100%; 95% confidence interval (CI), 90–128%). An oral mucosal immune cluster (burning mouth syndrome, lichen planus, candidiasis, and xerostomia) emerged as a clinically actionable community. Conclusions: Oral diseases form biologically coherent, age-evolving comorbidity communities, and pulpitis is the critical mediating intervention point in the caries-to-tooth-defect cascade. The framework provides a reusable network-medicine substrate for age- and sex-specific risk-stratified oral disease management. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biotechnology)
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19 pages, 4535 KB  
Article
Wideband Circularly Polarized Conformal Antenna with Physics-Informed Neural Network Modeling for IoBNT Capsule Endoscopy
by Pariya Nasirishehni, Mohammad (Behdad) Jamshidi and Mehdi Mehranpour
Bioengineering 2026, 13(6), 620; https://doi.org/10.3390/bioengineering13060620 - 26 May 2026
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Abstract
The convergence of artificial intelligence, biotechnology, and the Internet of Bio-Nano Things (IoBNT) is enabling the creation of a new generation of intelligent in-body medical devices for continuous diagnosis and monitoring. In this context, a compact, wideband, circularly polarized conformal microstrip antenna is [...] Read more.
The convergence of artificial intelligence, biotechnology, and the Internet of Bio-Nano Things (IoBNT) is enabling the creation of a new generation of intelligent in-body medical devices for continuous diagnosis and monitoring. In this context, a compact, wideband, circularly polarized conformal microstrip antenna is proposed for capsule endoscopy applications. The antenna is integrated along the inner wall of a 10 mm-diameter capsule and achieves an impedance bandwidth of 2.06–5.39 GHz (89.39%), maintaining stable matching under varying biological tissue conditions. A 3 dB axial ratio bandwidth (ARBW) of 2.31–3.14 GHz (30.45%) ensures reliable circular polarization and robust wireless communication in lossy and dynamic in-body environments. To extend beyond conventional electromagnetic analysis, a physics-informed neural network (PINN) framework is introduced to model the thermal response of biological tissues based on the governing bioheat equation. This AI-driven approach enables fast and generalizable prediction of temperature rise under varying operational conditions without repeated numerical simulations. At 2.45 GHz, the antenna exhibits a maximum gain of −31.1 dBi with a radiation efficiency of approximately −34 dB, consistent with in-body propagation constraints. Simulation and experimental results in realistic tissue phantoms, including muscle, small intestine, large intestine, and stomach, confirm stable wideband and polarization performance. Specific absorption rate (SAR) analysis demonstrates compliance with IEEE C95.1-2019 safety limits, while link budget evaluation validates reliable telemetry over a 1–3 m communication range. The integration of advanced antenna design with physics-informed machine learning provides a scalable framework for intelligent, safe, and adaptive IoBNT-enabled capsule endoscopy systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biotechnology)
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15 pages, 1138 KB  
Systematic Review
Diagnostic Support in Dentistry Through Artificial Intelligence: A Systematic Review
by Alessio Danilo Inchingolo, Grazia Marinelli, Arianna Fiore, Liviana Balestriere, Claudio Carone, Francesco Inchingolo, Massimo Corsalini, Daniela Di Venere, Andrea Palermo, Angelo Michele Inchingolo and Gianna Dipalma
Bioengineering 2025, 12(11), 1244; https://doi.org/10.3390/bioengineering12111244 - 13 Nov 2025
Cited by 5 | Viewed by 2335
Abstract
Background/Objectives: The integration of artificial intelligence (AI) into dental diagnostics is rapidly evolving, offering opportunities to improve diagnostic precision, reproducibility, and accessibility of care. This systematic review examined the clinical performance of AI-based diagnostic tools in dentistry compared with traditional methods, with [...] Read more.
Background/Objectives: The integration of artificial intelligence (AI) into dental diagnostics is rapidly evolving, offering opportunities to improve diagnostic precision, reproducibility, and accessibility of care. This systematic review examined the clinical performance of AI-based diagnostic tools in dentistry compared with traditional methods, with particular attention to radiographic assessment, orthodontic classification, periodontal disease detection, and other relevant specialties. Methods: Comprehensive searches of PubMed, Scopus, and Web of Science were carried out for articles published from January 2015 to June 2025, in accordance with PRISMA guidelines. Only English-language clinical studies investigating AI applications in dental diagnostics were included. Fifteen studies fulfilled the inclusion criteria and underwent quality appraisal and risk-of-bias assessment. Results: Across diverse dental fields, AI systems showed encouraging diagnostic capabilities. Radiographic algorithms enhanced lesion detection and anatomical landmark identification, while machine learning models successfully classified malocclusions and periodontal status. Photographic image analysis demonstrated potential in geriatric and preventive care. However, methodological variability, limited sample sizes, and the absence of external validation constrained generalizability. Study quality ranged from high to moderate, with some reports affected by bias or incomplete data reporting. Conclusions: AI holds considerable promise as an adjunct in dental diagnostics, particularly for imaging-based evaluation and clinical decision support. Broader clinical adoption will require methodological harmonization, rigorous multicenter trials, and validation of AI systems across diverse patient populations. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biotechnology)
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