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Bioengineering
Special Issues
Bioengineering Models and Methods for Disease Prevention and Innovative Treatment
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Bioengineering Models and Methods for Disease Prevention and Innovative Treatment

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3259

Special Issue Editors

Dr. Pierangelo Veltri

E-Mail Website
Guest Editor
Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, Rende, Italy
Interests: bioinformatics; proteomics; health informatics; clinical decision support systems; medical/clinical informatics; biomedical informatics
Dr. Patrizia Vizza

E-Mail Website
Guest Editor
Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
Interests: biomedical signal processing; medical image analysis; clinical decision support systems; medical/clinical informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Tradigo

E-Mail Website
Guest Editor
Computer Science, eCampus University, 22060 Novedrate, Italy
Interests: bioinformatics; computational proteomics and genomics; information extraction from health data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics algorithms and tools are relevant in the context of human health to identify biomarkers, predict onset, simulate molecular and protein interaction, evaluate treatment outcomes, and thus support the design of disease treatments and therapies.

Biomedical data modeling, processing and analysis can be used to analyze and correlate large datasets of information related to human health, allowing the identification of relevant features and patterns that can be useful to support clinicians in diagnosis and treatment as well as governments for health-related policies.

Clinical and biological data contain relevant information and patterns that must be extracted by a well-defined signal processing procedure to furnish quantitative measurements to physicians that can use these extracted information in decision support systems for scientific hypotheses and medical diagnoses. For instance, the application of artificial-intelligence-based modules is considered a relevant support for the analysis of biosignals and bioimages and their clinical-related extracted features in the early detection, investigation, diagnosis, classification, management and treatment of many pathological conditions and diseases, as well as in the context of the COVID-19 pandemic.

The design and definition of innovative biomedical informatics algorithms and biomedical data processing strategies are always highly required for the analysis of the huge quantity of available data to be applied in the human health and environmental context.

This Special Issue hosts research results regarding recent advances and trends concerning novel algorithms and tools for biomedical solutions as well as the analysis of biomedical signals and images in clinical practice.

Topics of interest include, but are not limited to:

  • Data modeling and management for health related information
  • Large-scale biological and biomedical databases
  • Biomedical data integration and solutions
  • Algorithms for health informatics
  • Prediction and inference models for biological and health-related information
  • Scientific workflows and process management in health informatics
  • High-performance solutions for bioinformatics and biomedicine
  • Architectures for bioinformatics and biomedicine
  • Clinical and biological signal management
  • Bioengineering solutions for epidemiological phenomena
  • Bioengineering solutions for human health

Dr. Pierangelo Veltri
Dr. Patrizia Vizza
Dr. Giuseppe Tradigo
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. 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

  • bioinformatics
  • biomedical signal processing
  • biomedical informatics
  • artificial intelligence
  • predictive algorithm
  • clinical decision support systems
  • early disease detection

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

Published Papers (3 papers)

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Research

22 pages, 3232 KiB  
Article
An Unsupervised Error Detection Methodology for Detecting Mislabels in Healthcare Analytics
by Pei-Yuan Zhou, Faith Lum, Tony Jiecao Wang, Anubhav Bhatti, Surajsinh Parmar, Chen Dan and Andrew K. C. Wong
Bioengineering 2024, 11(8), 770; https://doi.org/10.3390/bioengineering11080770 - 31 Jul 2024
Viewed by 640
Abstract
Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, [...] Read more.
Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data. Full article
(This article belongs to the Special Issue Bioengineering Models and Methods for Disease Prevention and Innovative Treatment)
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17 pages, 7620 KiB  
Article
HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA–Disease Association Prediction
by Daying Lu, Jian Li, Chunhou Zheng, Jinxing Liu and Qi Zhang
Bioengineering 2024, 11(7), 680; https://doi.org/10.3390/bioengineering11070680 - 4 Jul 2024
Viewed by 729
Abstract
Accumulating scientific evidence highlights the pivotal role of miRNA–disease association research in elucidating disease pathogenesis and developing innovative diagnostics. Consequently, accurately identifying disease-associated miRNAs has emerged as a prominent research topic in bioinformatics. Advances in graph neural networks (GNNs) have catalyzed methodological breakthroughs [...] Read more.
Accumulating scientific evidence highlights the pivotal role of miRNA–disease association research in elucidating disease pathogenesis and developing innovative diagnostics. Consequently, accurately identifying disease-associated miRNAs has emerged as a prominent research topic in bioinformatics. Advances in graph neural networks (GNNs) have catalyzed methodological breakthroughs in this field. However, existing methods are often plagued by data noise and struggle to effectively integrate local and global information, which hinders their predictive performance. To address this, we introduce HGTMDA, an innovative hypergraph learning framework that incorporates random walk with restart-based association masking and an enhanced GCN-Transformer model to infer miRNA–disease associations. HGTMDA starts by constructing multiple homogeneous similarity networks. A novel enhancement of our approach is the introduction of a restart-based random walk association masking strategy. By stochastically masking a subset of association data and integrating it with a GCN enhanced by an attention mechanism, this strategy enables better capture of key information, leading to improved information utilization and reduced impact of noisy data. Next, we build an miRNA–disease heterogeneous hypergraph and adopt an improved GCN-Transformer encoder to effectively solve the effective extraction of local and global information. Lastly, we utilize a combined Dice cross-entropy (DCE) loss function to guide the model training and optimize its performance. To evaluate the performance of HGTMDA, comprehensive comparisons were conducted with state-of-the-art methods. Additionally, in-depth case studies on lung cancer and colorectal cancer were performed. The results demonstrate HGTMDA’s outstanding performance across various metrics and its exceptional effectiveness in real-world application scenarios, highlighting the advantages and value of this method. Full article
(This article belongs to the Special Issue Bioengineering Models and Methods for Disease Prevention and Innovative Treatment)
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15 pages, 10607 KiB  
Article
REHABS: An Innovative and User-Friendly Device for Rehabilitation
by Patrizia Vizza, Nicola Marotta, Antonio Ammendolia, Pietro Hiram Guzzi, Pierangelo Veltri and Giuseppe Tradigo
Bioengineering 2024, 11(1), 5; https://doi.org/10.3390/bioengineering11010005 - 21 Dec 2023
Viewed by 1142
Abstract
Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual’s recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition [...] Read more.
Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual’s recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition as close to normal as possible. Innovative devices and solutions make the rehabilitation process of patients easier during their daily activities. Devices support physicians and physiotherapists in monitoring and measuring patients’ physical improvements during rehabilitation. In this context, we report the design and implementation of a low-cost rehabilitation system, which is a programmable device designed to support tele-rehabilitation of the upper limbs. The proposed system includes a mechanism to acquire and analyze data and signals related to rehabilitation processes. Full article
(This article belongs to the Special Issue Bioengineering Models and Methods for Disease Prevention and Innovative Treatment)
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