Advances in Biomedical Data Science: Methods and Applications

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.

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

Title: Exploring RNA-seq data through visualization techniques and tools: A systematic review for opportunities and limitations for clinical applications
Authors: Vibhuti Gupta; Cyruss Tsurgeon
Affiliation: Department of Computer science and data science, Meharry medical college
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 (including bulk and single cell RNA-seq methods), 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 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 2019 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 21% of studies have visualization techniques and tools for all types of RNA-seq data, 21% for circular RNA seq data, 55% for single cell RNA seq data, and only 3% are for bulk 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).