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Advances in Bioinformatics and Biomedical Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 8300

Special Issue Editors

Dr. Aristidis G. Vrahatis

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 49100 Corfu, Greece
Interests: machine learning; biomedical data mining; biological complex systems modeling; network medicine
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Panayiotis Vlamos

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 7 Tsirigoti Square, 49132 Corfu, Greece
Interests: biomarkers; Alzheimer’s disease; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Themis Exarchos

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, Corfu, Greece
Interests: health analytics; decision support systems; biomedical engineering; biomedical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Advances in Bioinformatics and Biomedical Engineering", presents a detailed examination of significant developments in technology, biology, and medicine. It emphasizes how these developments are crucial in transforming healthcare. The issue covers key topics such as healthcare information systems, biomedical imaging, computational omics, biomechanics, pharmacogenomics, precision medicine, and drug repurposing.

In the section on healthcare information systems, we investigate the impact of advanced data management and computational tools on improving patient care and medical processes. Biomedical imaging is also a major area of focus, where we look into the latest imaging technologies and their role in making diagnoses more accurate.

A substantial part of this issue is devoted to computational omics. Here, we explore how computational techniques are used to analyze and interpret large-scale biological data, particularly in genomics, proteomics, and metabolomics. This discussion extends into pharmacogenomics and precision medicine, where we discuss tailored medical treatments based on individual genetic information and health data.

The subject of biomechanics is examined in terms of its application in creating better medical devices and understanding the physical functioning of biological systems. Finally, we address drug repurposing, which involves finding new uses for existing drugs to treat different diseases.

This Special Issue aims to provide an academic and in-depth view of how bioinformatics and biomedical engineering are merging to create new pathways in medical science, leading to more effective and personalized healthcare solutions.

Dr. Aristidis G. Vrahatis
Prof. Dr. Panagiotis Vlamos
Dr. Themis Exarchos
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • healthcare information systems
  • biomedical imaging
  • computational omics
  • biomechanics
  • pharmacogenomics
  • precision medicine
  • drug repurposing

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Published Papers (6 papers)

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Research

16 pages, 3628 KiB  
Article
A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
by Sergii Babichev, Oleg Yarema, Igor Liakh and Nataliia Shumylo
Appl. Sci. 2025, 15(8), 4471; https://doi.org/10.3390/app15084471 - 18 Apr 2025
Viewed by 126
Abstract
The growing volume and complexity of gene expression data necessitate biologically meaningful and statistically robust methods for feature selection to enhance the effectiveness of disease diagnosis systems. The present study addresses this challenge by proposing a pipeline that integrates RNA-seq data preprocessing, differential [...] Read more.
The growing volume and complexity of gene expression data necessitate biologically meaningful and statistically robust methods for feature selection to enhance the effectiveness of disease diagnosis systems. The present study addresses this challenge by proposing a pipeline that integrates RNA-seq data preprocessing, differential gene expression analysis, Gene Ontology (GO) enrichment, and ensemble-based machine learning. The pipeline employs the non-parametric Kruskal–Wallis test to identify differentially expressed genes, followed by dual enrichment analysis using both Fisher’s exact test and the Kolmogorov–Smirnov test across three GO categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Genes associated with GO terms found significant by both tests were used to construct multiple gene subsets, including subsets based on individual categories, their union, and their intersection. Classification experiments using a random forest model, validated via 5-fold cross-validation, demonstrated that gene subsets derived from the CC category and the union of all categories achieved the highest accuracy and weighted F1-scores, exceeding 0.97 across 14 cancer types. In contrast, subsets derived from BP, MF, and especially their intersection exhibited lower performance. These results confirm the discriminative power of spatially localized gene annotations and underscore the value of integrating statistical and functional information into gene selection. The proposed approach improves the reliability of biomarker discovery and supports downstream analyses such as clustering and biclustering, providing a strong foundation for developing precise diagnostic tools in personalized medicine. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Biomedical Engineering)
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22 pages, 5584 KiB  
Article
Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model
by Abeer Fayez Al Bataineh, Khalid M. O. Nahar, Hayel Khafajeh, Ghassan Samara, Raed Alazaidah, Ahmad Nasayreh, Ayah Bashkami, Hasan Gharaibeh and Waed Dawaghreh
Appl. Sci. 2024, 14(22), 10154; https://doi.org/10.3390/app142210154 - 6 Nov 2024
Cited by 1 | Viewed by 1839
Abstract
Brain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges [...] Read more.
Brain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges to achieve an accurate and final diagnosis. Effective brain cancer detection is crucial for patients’ safety and health. Deep learning systems provide the capability to assist radiologists in quickly and accurately detecting diagnoses. This study presents an innovative deep learning approach that utilizes the Swin Transformer. The suggested method entails integrating the Swin Transformer with the pretrained deep learning model Resnet50V2, called (SwT+Resnet50V2). The objective of this modification is to decrease memory utilization, enhance classification accuracy, and reduce training complexity. The self-attention mechanism of the Swin Transformer identifies distant relationships and captures the overall context. Resnet 50V2 improves both accuracy and training speed by extracting adaptive features from the Swin Transformer’s dependencies. We evaluate the proposed framework using two publicly accessible brain magnetic resonance imaging (MRI) datasets, each including two and four distinct classes, respectively. Employing data augmentation and transfer learning techniques enhances model performance, leading to more dependable and cost-effective training. The suggested model achieves an impressive accuracy of 99.9% on the binary-labeled dataset and 96.8% on the four-labeled dataset, outperforming the VGG16, MobileNetV2, Resnet50V2, EfficientNetV2B3, ConvNeXtTiny, and convolutional neural network (CNN) algorithms used for comparison. This demonstrates that the Swin transducer, when combined with Resnet50V2, is capable of accurately diagnosing brain tumors. This method leverages the combination of SwT+Resnet50V2 to create an innovative diagnostic tool. Radiologists have the potential to accelerate and improve the detection of brain tumors, leading to improved patient outcomes and reduced risks. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Biomedical Engineering)
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12 pages, 3097 KiB  
Article
Exploring the Association between Pro-Inflammation and the Early Diagnosis of Alzheimer’s Disease in Buccal Cells Using Immunocytochemistry and Machine Learning Techniques
by Konstantinos Lazaros, Maria Gonidi, Nafsika Kontara, Marios G. Krokidis, Aristidis G. Vrahatis, Themis Exarchos and Panagiotis Vlamos
Appl. Sci. 2024, 14(18), 8372; https://doi.org/10.3390/app14188372 - 18 Sep 2024
Viewed by 1158
Abstract
The progressive aging of the global population and the high impact of neurodegenerative diseases, such as Alzheimer’s disease (AD), underscore the urgent need for innovative diagnostic and therapeutic strategies. AD, the most prevalent neurodegenerative disorder among the elderly, is expected to affect 75 [...] Read more.
The progressive aging of the global population and the high impact of neurodegenerative diseases, such as Alzheimer’s disease (AD), underscore the urgent need for innovative diagnostic and therapeutic strategies. AD, the most prevalent neurodegenerative disorder among the elderly, is expected to affect 75 million people in developing countries by 2030. Despite extensive research, the precise etiology of AD remains elusive due to its heterogeneity and complexity. The key pathological features of AD, including amyloid-beta plaques and hyperphosphorylated tau protein, are established years before clinical symptoms appear. Recent studies highlight the pivotal role of neuroinflammation in AD pathogenesis, with the chronic activation of the brain’s immune system contributing to the disease’s progression. Pro-inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, are elevated in AD and mild cognitive impairment (MCI) patients, suggesting a strong link between peripheral inflammation and CNS degeneration. There is a pressing need for minimally invasive, cost-effective diagnostic methods. Buccal mucosa cells and saliva, which share an embryological origin with the CNS, show promise for AD diagnosis and prognosis. This study integrates cellular observations with advanced data processing and machine learning to identify significant biomarkers and patterns, aiming to enhance the early diagnosis and prevention strategies for AD. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Biomedical Engineering)
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15 pages, 7956 KiB  
Article
Modeling the Stress–Strain State of a Filled Human Bladder
by Marina Barulina, Tatyana Timkina, Yaroslav Ivanov, Vladimir Masliakov, Maksim Polidanov and Kirill Volkov
Appl. Sci. 2024, 14(17), 7562; https://doi.org/10.3390/app14177562 - 27 Aug 2024
Cited by 2 | Viewed by 1378
Abstract
In this paper, the problems of modeling the human bladder and its stress–strain state under an external static influence are considered. A method for the identification of the anisotropic biomechanical characteristics of the bladder tissue is proposed. An FEM model was created, which [...] Read more.
In this paper, the problems of modeling the human bladder and its stress–strain state under an external static influence are considered. A method for the identification of the anisotropic biomechanical characteristics of the bladder tissue is proposed. An FEM model was created, which takes into account the fact that the bladder is surrounded by fiber, affected by surrounding organs, and partially protected by pelvic bones. The model considers the presence of constant hydrostatic pressure on the walls of the bladder when it is full. It has been shown that the isotropic mechanical characteristics of biological tissue can be used for studying the deformed state of a filled bladder if a filled bladder of 300 mL is considered as the initial non-deformed stage. This was shown by the modeling and verification of the effect of the external static force on the bladder. Numerical experiments were conducted based on the constructed model. To validate the results obtained, a series of natural experiments on the effect of external pressure on the bladder under ultrasound control were conducted. In the future, there are plans to use the constructed model to study rupture deformations of the bladder under the influence of static and dynamic loads. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Biomedical Engineering)
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14 pages, 776 KiB  
Article
Exhaustive Variant Interaction Analysis Using Multifactor Dimensionality Reduction
by Gonzalo Gómez-Sánchez, Lorena Alonso, Miguel Ángel Pérez, Ignasi Morán, David Torrents and Josep Ll. Berral
Appl. Sci. 2024, 14(12), 5136; https://doi.org/10.3390/app14125136 - 13 Jun 2024
Viewed by 999
Abstract
One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease–variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction [...] Read more.
One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease–variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. In particular, in a background of complex diseases, these interactions can be directly linked to the disorder and may play an important role in disease development. Although their study has been suggested to help complete the understanding of the genetic bases of complex diseases, this still represents a big challenge due to large computing demands. Here, we take advantage of high-performance computing technologies to tackle this problem by using a combination of machine learning methods and statistical approaches. As a result, we created a containerized framework that uses multifactor dimensionality reduction (MDR) to detect pairs of variants associated with type 2 diabetes (T2D). This methodology was tested on the Northwestern University NUgene project cohort using a dataset of 1,883,192 variant pairs with a certain degree of association with T2D. Out of the pairs studied, we identified 104 significant pairs: two of which exhibit a potential functional relationship with T2D. These results place the proposed MDR method as a valid, efficient, and portable solution to study variant interaction in real reduced genomic datasets. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Biomedical Engineering)
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14 pages, 3312 KiB  
Article
Development of an ICT Laparoscopy System with Motion-Tracking Technology for Solo Laparoscopic Surgery: A Feasibility Study
by Miso Lee, Jinwoo Oh, Taegeon Kang, Suhyun Lim, Munhwan Jo, Min-Jae Jeon, Hoyul Lee, Inhwan Hwang, Shinwon Kang, Jin-Hee Moon and Jae-Seok Min
Appl. Sci. 2024, 14(11), 4622; https://doi.org/10.3390/app14114622 - 28 May 2024
Viewed by 1845
Abstract
The increasing demand for laparoscopic surgery due to its cosmetic benefits and rapid post-surgery recovery is juxtaposed with a shortage of surgical support staff. This juxtaposition highlights the necessity for improved camera management in laparoscopic procedures, encompassing positioning, zooming, and focusing. Our feasibility [...] Read more.
The increasing demand for laparoscopic surgery due to its cosmetic benefits and rapid post-surgery recovery is juxtaposed with a shortage of surgical support staff. This juxtaposition highlights the necessity for improved camera management in laparoscopic procedures, encompassing positioning, zooming, and focusing. Our feasibility study introduces the information and communications technology (ICT) laparoscopy system designed to aid solo laparoscopic surgery. This system tracks a surgeon’s body motion using a controller, manipulating an embedded camera to focus on specific surgical areas. It comprises a camera module, a camera movement controller, and a motor within the main body, operating connected wires according to controller commands for camera movement. Surgeon movements are detected by an inertial measurement unit (IMU) sensor, facilitating precise camera control. Additional features include a foot pedal switch for motion tracking, a dedicated trocar for main body stability, and a display module. The system’s effectiveness was evaluated using an abdomen phantom model and animal experimentation with a porcine model. The camera responded to human movement within 100 ms, a delay that does not significantly affect procedural performance. The ICT laparoscopy system with advanced motion-tracking technology is a promising tool for solo laparoscopic surgery, potentially improving surgical outcomes and overcoming staff shortages. Full article
(This article belongs to the Special Issue Advances in Bioinformatics and Biomedical Engineering)
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