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Bioengineering
Special Issues
Advances in Biomedical Data Science: Methods and Applications
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Advances in Biomedical Data Science: Methods and Applications

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3137

Special Issue Editors

Dr. Qingguo Wang

E-Mail Website
Guest Editor
Department of Computer Science & Data Science, Meharry Medical College, Nashville, TN 37208, USA
Interests: machine learning; data science; computational biology; next-generation sequencing (NGS); cancer genomics
Dr. Youping Deng

E-Mail Website
Guest Editor
Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, 651 Ilalo Street, Honolulu, HI 96813, USA
Interests: bioinformatics; genomics; biomarker; systems biology; biomedical informatics; cancer
Special Issues, Collections and Topics in MDPI journals
Dr. Joshua Burkhart

E-Mail Website
Guest Editor
Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA
Interests: bioinformatics; biomedical informatics; cancer; genomics; systems biology

Special Issue Information

Dear Colleagues,

With data science being increasingly crucial for leveraging big data to gain a competitive edge, accelerate scientific discovery, and advance public health, we are organizing a Special Issue of Bioengineering entitled “Advances in Biomedical Data Science: Methods and Applications”. The aim is to highlight the advances in biomedical data science, new methodologies, and their applications to sciences, technologies, bioengineering, medicine, health, and healthcare. Bioengineering is an international, scientific, peer-reviewed, open access journal on the science and technology of bioengineering, published monthly online. For detailed information on the journal, we refer you to https://www.mdpi.com/journal/bioengineering. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Advances in biomedical data science;
  • Data science for biomedical wearable devices;
  • New datasets, AI/ML tools, and novel data analyst methods;
  • Applications of data science to bioengineering, medicine, public health, and minority health;
  • Bioinformatics, computational biology, and automation of bioprocess and biosystems;
  • Big data analytics for biosystems: gene set and pathway analyses, visualization, modeling, and simulation;
  • Data science capacity building in minority serving institutions;
  • Development of diverse data science workforce;
  • Natural Language processing.

Submission of full manuscripts of original research, comprehensive reviews, and/or short communications on any of these topics is strongly encouraged. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere. 

Dr. Qingguo Wang
Prof. Dr. Youping Deng
Dr. Joshua Burkhart
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

  • biomedical data science
  • computational biology
  • biomedical informatics
  • capacity building
  • data science workforce development

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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17 pages, 3176 KiB  
Article
Taba Binary, Multinomial, and Ordinal Regression Models: New Machine Learning Methods for Classification
by Mohammad Tabatabai, Derek Wilus, Chau-Kuang Chen, Karan P. Singh and Tim L. Wallace
Bioengineering 2025, 12(1), 2; https://doi.org/10.3390/bioengineering12010002 - 24 Dec 2024
Viewed by 796
Abstract
The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a [...] Read more.
The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a Mayo Clinic study were analyzed. The results were then compared with an artificial neural network (ANN), random forest (RF), logistic regression (LR), and probit analysis (PA). The results using cirrhosis data revealed that the Taba regression model could be a competitor to other classification models based on the true positive rate, F-score, accuracy, and area under the receiver operating characteristic curve (AUC). Taba regression can be used by researchers and practitioners as an alternative method of classification in machine learning. In conclusion, the Taba regression provided a reliable result with respect to accuracy, recall, F-score, and AUC when applied to the cirrhosis data. Full article
(This article belongs to the Special Issue Advances in Biomedical Data Science: Methods and Applications)
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25 pages, 340 KiB  
Systematic Review
Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications
by Farhana Manzoor, Cyruss A. Tsurgeon and Vibhuti Gupta
Bioengineering 2025, 12(1), 56; https://doi.org/10.3390/bioengineering12010056 - 12 Jan 2025
Viewed by 1567
Abstract
RNA sequencing (RNA-seq) has emerged as a prominent resource for transcriptomic analysis due to its ability to measure gene expression in a highly sensitive and accurate manner. With the increasing availability of RNA-seq data analysis from clinical studies and patient samples, the development [...] Read more.
RNA sequencing (RNA-seq) has emerged as a prominent resource for transcriptomic analysis due to its ability to measure gene expression in a highly sensitive and accurate manner. With the increasing availability of RNA-seq data analysis from clinical studies and patient samples, the development of effective visualization tools for RNA-seq analysis has become increasingly important to help clinicians and biomedical researchers better understand the complex patterns of gene expression associated with health and disease. This review aims to outline the current state-of-the-art data visualization techniques and tools commonly used to frame clinical inferences from RNA-seq data and point out their benefits, applications, and limitations. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “RNA-seq”, “visualization”, “plots”, and “clinical”. Only full-text studies reported between 2017 and 2024 were included for analysis. Following PRISMA guidelines, a total of 126 studies were identified, of which 33 studies met the inclusion criteria. We found that 18% of studies have visualization techniques and tools for circular RNA-seq data, 56% for single-cell RNA-seq data, 23% for bulk RNA-seq data, and 3% for long non-coding RNA-seq data. Overall, this review provides a comprehensive overview of the common visualization tools and their potential applications, which is a useful resource for researchers and clinicians interested in using RNA-seq data for various clinical purposes (e.g., diagnosis or prognosis). Full article
(This article belongs to the Special Issue Advances in Biomedical Data Science: Methods and Applications)
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