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Technologies
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Breakthroughs in Bioinformatics and Biomedical Engineering
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Breakthroughs in Bioinformatics and Biomedical Engineering

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 7410

Special Issue Editors

Prof. Dr. Panayiotis Tsanakas

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15772 Athens, Greece
Interests: biomedical informatics; high-performance systems; distributed systems and algorithms; cloud/edge computing
Dr. Evangelos Makris

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, 15772 Athens, Greece
Interests: bioinformatics; pattern recognition; artificial intelligence; activity recognition

Special Issue Information

Dear Colleagues,

This Special Issue explores cutting-edge advancements in bioinformatics and biomedical engineering, focusing on leveraging technology to solve challenges in medicine and biology. We invite high-quality original research, reviews, and case studies demonstrating novel applications of artificial intelligence (AI), machine learning (ML), and computational methods in healthcare, bioinformatics, and medical imaging. Topics will highlight innovations in bioinformatics, medical image analysis, and AI-driven diagnostics that enhance the understanding and treatment of diseases.

Prof. Dr. Panayiotis Tsanakas
Dr. Evangelos Makris
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies 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 1800 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

  • bioinformatics
  • medical image analysis
  • artificial intelligence in medicine
  • deep learning
  • machine learning in healthcare
  • computational genomics
  • RNA sequencing
  • predictive modeling
  • wearable health devices

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Published Papers (5 papers)

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Research

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22 pages, 12315 KB  
Article
An Open-Source Neonatal Phototherapy Device
by Joshua Givans, Augustine Waswa, Janiffer Nyambura, Gidraf Njoroge, Gordon Macharia, June Madete and Joshua M. Pearce
Technologies 2025, 13(11), 499; https://doi.org/10.3390/technologies13110499 - 31 Oct 2025
Viewed by 1592
Abstract
Severe neonatal hyperbilirubinemia (SNH) (jaundice) is responsible for over 114,000 preventable neonatal deaths annually, as the technology that can treat the condition is cost-prohibitive for low- and middle-income countries. In this study an open-source neonatal phototherapy device (NPTD) to treat SNH was designed, [...] Read more.
Severe neonatal hyperbilirubinemia (SNH) (jaundice) is responsible for over 114,000 preventable neonatal deaths annually, as the technology that can treat the condition is cost-prohibitive for low- and middle-income countries. In this study an open-source neonatal phototherapy device (NPTD) to treat SNH was designed, built, and validated against the phototherapy technical specifications set by the American Academy of Pediatrics and UNICEF. The open-source device can be built for a tenth of the cost of the least expensive proprietary one on the market, with treatment metrics equivalent to or exceeding commercial devices available in developed nations. This device, whose material costs are USD 93.00, was shown to deliver an irradiance up to 80 µW/cm2/nm, within the acceptable wavelength range of 420–500 nm. It was further demonstrated that the unit could deliver a uniform distribution of irradiance (34.5 ± 4.3 µW/cm2/nm) over a surface area exceeding 3200 cm2. These findings show that the open-source NPTD is capable of delivering accurate, consistent, and reliable irradiances for the management of SNH. By releasing full documentation in an open-source manner, the device may be broadly used to ensure affordable and consistent low-cost means of improving the quality of care for SNH. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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20 pages, 1298 KB  
Article
NCC—An Efficient Deep Learning Architecture for Non-Coding RNA Classification
by Konstantinos Vasilas, Evangelos Makris, Christos Pavlatos and Ilias Maglogiannis
Technologies 2025, 13(5), 196; https://doi.org/10.3390/technologies13050196 - 12 May 2025
Cited by 1 | Viewed by 1695
Abstract
In this paper, an efficient deep-learning architecture is proposed, aiming to classify a significant category of RNA, the non-coding RNAs (ncRNAs). These RNAs participate in various biological processes and play an important role in gene regulation as well. Because of their diverse nature, [...] Read more.
In this paper, an efficient deep-learning architecture is proposed, aiming to classify a significant category of RNA, the non-coding RNAs (ncRNAs). These RNAs participate in various biological processes and play an important role in gene regulation as well. Because of their diverse nature, the task of classifying them is a hard one in the bioinformatics domain. Existing classification methods often rely on secondary or tertiary RNA structures, which are computationally expensive to predict and prone to errors, especially for complex or novel ncRNA sequences. To address these limitations, a deep neural network classifier called NCC is proposed, which focuses solely on primary RNA sequence information. This deep neural network is appropriately trained to identify patterns in ncRNAs, leveraging well-known datasets, which are publicly available. Additionally, a ten times larger dataset than the available ones is created for better training and testing. In terms of performance, the suggested model showcases a 6% enhancement in precision compared to prior state-of-the-art systems, with an accuracy level of 92.69%, in the existing dataset. In the larger one, its accuracy rate exceeded 98%, outperforming all related tools, pointing to high prediction capability, which can act as a base for further findings in ncRNA analysis and the genomics field in general. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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37 pages, 3382 KB  
Article
Multi-Domain Feature Incorporation of Lightweight Convolutional Neural Networks and Handcrafted Features for Lung and Colon Cancer Diagnosis
by Omneya Attallah
Technologies 2025, 13(5), 173; https://doi.org/10.3390/technologies13050173 - 25 Apr 2025
Cited by 10 | Viewed by 1485
Abstract
This study presents a computer-aided diagnostic (CAD) framework that integrates multi-domain features through a hybrid methodology. The system uses several light deep networks (EfficientNetB0, MobileNet, and ResNet-18), which feature fewer layers and parameters, unlike traditional systems that depend on a single, parameter-complex deep [...] Read more.
This study presents a computer-aided diagnostic (CAD) framework that integrates multi-domain features through a hybrid methodology. The system uses several light deep networks (EfficientNetB0, MobileNet, and ResNet-18), which feature fewer layers and parameters, unlike traditional systems that depend on a single, parameter-complex deep network. Additionally, it employs several handcrafted feature extraction techniques. It systematically assesses the diagnostic power of deep features only, handcrafted features alone, and both deep and handcrafted features combined. Furthermore, it examines the influence of combining deep features from multiple CNNs with distinct handcrafted features on diagnostic accuracy, providing insights into the effectiveness of this hybrid approach for classifying lung and colon cancer. To achieve this, the proposed CAD employs non-negative matrix factorization for lowering the dimension of the spatial deep feature sets. In addition, these deep features obtained from each network are distinctly integrated with handcrafted features sourced from temporal statistical attributes and texture-based techniques, including gray-level co-occurrence matrix and local binary patterns. Moreover, the CAD integrates the deep attributes of the three deep networks with the handcrafted attributes. It also applies feature selection based on minimum redundancy maximum relevance to the integrated deep and handcrafted features, guaranteeing optimal computational efficiency and high diagnostic accuracy. The results indicated that the suggested CAD system attained remarkable accuracy, reaching 99.7% using multi-modal features. The suggested methodology, when compared to present CAD systems, either surpassed or was closely aligned with state-of-the-art methods. These findings highlight the efficacy of incorporating multi-domain attributes of numerous lightweight deep learning architectures and multiple handcrafted features. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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14 pages, 12491 KB  
Article
Biomechanical Evaluation of Elliptical Leaf Spring Prosthetics for Unilateral Transtibial Amputees During Dynamic Activities
by Qiu-Qiong Shi, Kit-Lun Yick, Chu-Hao Li, Chi-Yung Tse and Chi-Hang Hui
Technologies 2025, 13(4), 129; https://doi.org/10.3390/technologies13040129 - 30 Mar 2025
Cited by 2 | Viewed by 1518
Abstract
This study explores the biomechanical impact of an elliptical leaf spring (ELS) foot on individuals with unilateral below-knee amputation. The ELS-foot, constructed with carbon fiber leaf springs and an ethylene-vinyl acetate rocker bottom sole, aims to balance energy storge and dissipation for effective [...] Read more.
This study explores the biomechanical impact of an elliptical leaf spring (ELS) foot on individuals with unilateral below-knee amputation. The ELS-foot, constructed with carbon fiber leaf springs and an ethylene-vinyl acetate rocker bottom sole, aims to balance energy storge and dissipation for effective cushioning and energy management. Six participants were recruited and visited the laboratory twice within a 3-to-5-day interval. The ELS-foot is compared with their own prosthesis through various mobility and balance tests, including the Timed Up and Go test, Four Square Step Test, 10 m walk test, Berg Balance Test, eyes-closed standing test, Tandem Test, jumping and walking test, and a subjective evaluation. Passive-reflective markers are placed on the participants according to the plug-in full body model. An eight-camera motion capture system synced with two force plates mounted under a walkway is used for the gait analysis. The results show that participants move faster during the Four Square Step Test and demonstrate better balance during the eyes-closed standing test and Tandem Test and jump higher with the ELS-foot. The unique ELS-foot design mechanism and rocker bottom sole facilitates better energy transfer and stability, thus enhancing the postural stability. These findings offer valuable insights for future prosthetic technology advancements. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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Review

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36 pages, 1696 KB  
Review
Deep Learning in Cardiovascular Tissue Engineering: A Review on Current Advances and Future Perspectives
by Dumitru-Daniel Bonciog, Adriana Berdich, Liliana Mâțiu-Iovan and Valentin Laurențiu Ordodi
Technologies 2026, 14(1), 29; https://doi.org/10.3390/technologies14010029 - 1 Jan 2026
Viewed by 532
Abstract
The development of cardiovascular tissue engineering is a promising area of study in regenerative medicine, offering innovative solutions for restoring damaged cardiac structures. However, traditional methods face multiple limitations, including the complexity of scaffolds, optimal recellularization, and functional tissue maturation. At the same [...] Read more.
The development of cardiovascular tissue engineering is a promising area of study in regenerative medicine, offering innovative solutions for restoring damaged cardiac structures. However, traditional methods face multiple limitations, including the complexity of scaffolds, optimal recellularization, and functional tissue maturation. At the same time, deep learning has demonstrated significant potential in biomedicine and is increasingly being explored to optimize processes. This review examines recent benefits in cardiovascular tissue engineering and the applicability of deep learning in this domain, highlighting the benefits of artificial intelligence (AI) algorithms in scaffold modeling, cellular interaction analysis, and tissue regeneration prediction. Additionally, we discuss major challenges in integrating AI, such as the lack of large, standardized datasets; the need for interpretable models for clinical use; and ethical and regulatory constraints. Despite these limitations, recent progress in AI and the availability of advanced machine learning techniques provide promising perspectives for transforming regenerative medicine. Future research should focus on improving access to relevant data, developing explainable AI models, and integrating these technologies into personalized medicine, ultimately accelerating the progression of cardiovascular tissue engineering from an experimental stage to clinical utilization. Full article
(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)
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