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Bioengineering
Special Issues
Applications of Genomic Technology in Disease Outcome Prediction
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Applications of Genomic Technology in Disease Outcome Prediction

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Cellular and Molecular Bioengineering".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4937

Special Issue Editor

Prof. Dr. Sulev Koks

E-Mail Website
Guest Editor
1. Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA 6150, Australia
2. Perron Institute for Neurological and Translational Science, Perth, WA 6009, Australia
Interests: genomics; polygenic inheritance; medical genomics; genetic pathology; neurodegenerative diseases; complex diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the enormous volume of genetic information obtained from technologies that can provide insights into the genetic factors influencing the development and progression of disease.

By analyzing genomic data in conjunction with clinical information, researchers and healthcare professionals can identify the genetic markers associated with specific diseases and their outcomes. These markers may include specific gene mutations, gene expression patterns, or variations in the genome. Studying these markers enables patients who are at risk of developing certain diseases or experiencing poor treatment responses to be identified.

The application of genomic technology in disease outcome prediction has the potential to revolutionize personalized medicine. By employing this approach, physicians can develop targeted treatment plans based on an individual's genetic profile, leading to more effective treatments and improved patient outcomes. Additionally, disease risk prediction can aid in early detection and prevention efforts, as individuals identified as high-risk can undergo regular monitoring or preventative interventions.

However, challenges remain in translating genomic data into clinically actionable information. The interpretation of genomic data requires expertise in bioinformatics, statistical analysis, and clinical medicine. Furthermore, ethical considerations must be taken into account, such as protecting patient privacy and ensuring adequate informed consent.

Continued research in this field holds promise for a future in which the application of genomic technology will enhance the accuracy of disease outcome prediction, leading to personalized, targeted treatments that improve patient care and outcomes.

Prof. Dr. Sulev Koks
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 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

  • genomic technology
  • disease outcome prediction
  • genetic analysis
  • genetic variations
  • genome-wide association studies (GWAS)
  • genetic risk factors
  • biomarkers
  • genetic risk score
  • next-generation sequencing (NGS)
  • rare genetic variants
  • personalized medicine
  • treatment strategies.

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Published Papers (3 papers)

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Research

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9 pages, 3264 KiB  
Article
Development of a Low-Cost and Easy-Assembly Capillary Electrophoresis System for Separation of DNA
by Jiawen Li, Shuaiqiang Fan, Jiandong Zhu, Bo Yang, Zhenqing Li, Dawei Zhang and Yoshinori Yamaguchi
Bioengineering 2025, 12(3), 303; https://doi.org/10.3390/bioengineering12030303 - 17 Mar 2025
Viewed by 338
Abstract
Capillary electrophoresis based on laser-induced fluorescence (CE-LIF) plays an important role in the analysis of nucleic acids. However, the commercial CE-LIF is not only quite expensive but also inflexible, thus hindering its widespread use in the lab. Herein, we proposed a compact, low-cost, [...] Read more.
Capillary electrophoresis based on laser-induced fluorescence (CE-LIF) plays an important role in the analysis of nucleic acids. However, the commercial CE-LIF is not only quite expensive but also inflexible, thus hindering its widespread use in the lab. Herein, we proposed a compact, low-cost, and flexible CE-LIF system. We also investigated its stability by separating the DNA ladders. Experiments demonstrated that the relative standard error of the relative fluorescence intensity and migration time was lower than 6.2% and 1.1%, respectively. The aperture size of the light source illuminating the capillary can affect the separation performance. Smaller apertures offer higher resolution length for the adjacent DNA fragments but may reduce the number of theoretical plates. Various fluorescent dyes (e.g., SYBR Green I, Gel Green, EvaGreen) can be employed in the self-built system. The limit of detection of dsDNA was as low as 0.05 ng/μL. The working range for DNA was 0.05 ng/μL~10 ng/μL. Finally, we have successfully separated the PCR products of the target gene of Porphyromonas gingivalis and Candida albicans in the home-built CE system. Such a robust CE-LIF system is easy to assemble in the lab. The total cost of the assembled CE system did not exceed 1100 USD. We believe this work can advance the application of CE and hope it will facilitate the easy assembly of flexible CE instruments in labs. Full article
(This article belongs to the Special Issue Applications of Genomic Technology in Disease Outcome Prediction)
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29 pages, 5605 KiB  
Article
Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach
by Illia Mushta, Sulev Koks, Anton Popov and Oleksandr Lysenko
Bioengineering 2025, 12(1), 11; https://doi.org/10.3390/bioengineering12010011 - 27 Dec 2024
Viewed by 942
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate [...] Read more.
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes. Full article
(This article belongs to the Special Issue Applications of Genomic Technology in Disease Outcome Prediction)
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Review

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18 pages, 5759 KiB  
Review
Application and Technical Challenges in Design, Cloning, and Transfer of Large DNA
by Song Bai, Han Luo, Hanze Tong and Yi Wu
Bioengineering 2023, 10(12), 1425; https://doi.org/10.3390/bioengineering10121425 - 15 Dec 2023
Cited by 1 | Viewed by 3242
Abstract
In the field of synthetic biology, rapid advancements in DNA assembly and editing have made it possible to manipulate large DNA, even entire genomes. These advancements have facilitated the introduction of long metabolic pathways, the creation of large-scale disease models, and the design [...] Read more.
In the field of synthetic biology, rapid advancements in DNA assembly and editing have made it possible to manipulate large DNA, even entire genomes. These advancements have facilitated the introduction of long metabolic pathways, the creation of large-scale disease models, and the design and assembly of synthetic mega-chromosomes. Generally, the introduction of large DNA in host cells encompasses three critical steps: design-cloning-transfer. This review provides a comprehensive overview of the three key steps involved in large DNA transfer to advance the field of synthetic genomics and large DNA engineering. Full article
(This article belongs to the Special Issue Applications of Genomic Technology in Disease Outcome Prediction)
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