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BioMedInformatics
Special Issues
Editor's Choice Series for the Computational Biology and Medicine Section
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Editor's Choice Series for the Computational Biology and Medicine Section

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Computational Biology and Medicine".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2605

Special Issue Editor

Dr. Hans Binder

E-Mail Website
Guest Editor
Interdisciplinary Centre for Bioinformatics, Leipzig University, Haertelstr. 16–18, D-04107 Leipzig, Germany
Interests: genome medicine; computational biology; genomic regulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Editor's Choice Series for the Computational Biology and Medicine Section presents an exclusive collection highlighting groundbreaking methodologies at the intersection of computational sciences and biomedical research. This curated series encapsulates a diverse spectrum of innovative techniques, tools, and approaches shaping the future of computational biology and its profound impact on medical advancements.

Encompassing fields such as genomics, systems biology, structural biology, data analytics, and machine learning, this series offers an in-depth exploration of methodologies revolutionizing our understanding of biological systems. From novel algorithms for omics data analysis to predictive modeling in precision medicine, these articles elucidate the transformative potential of computational approaches in addressing complex medical challenges.

Please note that this series will not consider submissions for brief reports, instead emphasizing comprehensive investigations and critical analyses of methodologies and their applications in computational biology and medicine.

Dr. Hans Binder
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

  • genomics
  • systems biology
  • structural biology
  • data analytics
  • machine learning
  • omics
  • data analysis
  • precision medicine
  • computational approaches
  • biomedical research
  • methodological innovations

Published Papers (4 papers)

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Research

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13 pages, 1060 KiB  
Article
A Computational Approach to Demonstrate the Control of Gene Expression via Chromosomal Access in Colorectal Cancer
by Caleb J. Pecka, Ishwor Thapa, Amar B. Singh and Dhundy Bastola
BioMedInformatics 2024, 4(3), 1822-1834; https://doi.org/10.3390/biomedinformatics4030100 - 2 Aug 2024
Viewed by 267
Abstract
Background: Improved technologies for chromatin accessibility sequencing such as ATAC-seq have increased our understanding of gene regulation mechanisms, particularly in disease conditions such as cancer. Methods: This study introduces a computational tool that quantifies and establishes connections between chromatin accessibility, transcription factor binding, [...] Read more.
Background: Improved technologies for chromatin accessibility sequencing such as ATAC-seq have increased our understanding of gene regulation mechanisms, particularly in disease conditions such as cancer. Methods: This study introduces a computational tool that quantifies and establishes connections between chromatin accessibility, transcription factor binding, transcription factor mutations, and gene expression using publicly available colorectal cancer data. The tool has been packaged using a workflow management system to allow biologists and researchers to reproduce the results of this study. Results: We present compelling evidence linking chromatin accessibility to gene expression, with particular emphasis on SNP mutations and the accessibility of transcription factor genes. Furthermore, we have identified significant upregulation of key transcription factor interactions in colon cancer patients, including the apoptotic regulation facilitated by E2F1, MYC, and MYCN, as well as activation of the BCL-2 protein family facilitated by TP73. Conclusion: This study demonstrates the effectiveness of the computational tool in linking chromatin accessibility to gene expression and highlights significant transcription factor interactions in colorectal cancer. The code for this project is openly available on GitHub. Full article
(This article belongs to the Special Issue Editor's Choice Series for the Computational Biology and Medicine Section)
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18 pages, 4357 KiB  
Article
Harnessing Immunoinformatics for Precision Vaccines: Designing Epitope-Based Subunit Vaccines against Hepatitis E Virus
by Elijah Kolawole Oladipo, Emmanuel Oluwatobi Dairo, Comfort Olukemi Bamigboye, Ayodeji Folorunsho Ajayi, Olugbenga Samson Onile, Olumuyiwa Elijah Ariyo, Esther Moradeyo Jimah, Olubukola Monisola Oyawoye, Julius Kola Oloke, Bamidele Abiodun Iwalokun, Olumide Faith Ajani and Helen Onyeaka
BioMedInformatics 2024, 4(3), 1620-1637; https://doi.org/10.3390/biomedinformatics4030088 - 26 Jun 2024
Viewed by 936
Abstract
Background/Objectives: Hepatitis E virus (HEV) is an RNA virus recognized to be spread mainly by fecal-contaminated water. Its infection is known to be a serious threat to public health globally, mostly in developing countries, in which Africa is one of the regions sternly [...] Read more.
Background/Objectives: Hepatitis E virus (HEV) is an RNA virus recognized to be spread mainly by fecal-contaminated water. Its infection is known to be a serious threat to public health globally, mostly in developing countries, in which Africa is one of the regions sternly affected. An African-based vaccine is necessary to actively prevent HEV infection. Methods: This study developed an in silico epitope-based subunit vaccine, incorporating CTL, HTL, and BL epitopes with suitable linkers and adjuvants. Results: The in silico-designed vaccine construct proved immunogenic, non-allergenic, and non-toxic and displayed appropriate physicochemical properties with high solubility. The 3D structure was modeled and subjected to protein docking with Toll-like receptors 2, 3, 4, 6, 8, and 9, which showed a stable binding efficacy, and the dynamics simulation indicated steady interaction. Furthermore, the immune simulation predicted that the designed vaccine would instigate immune responses when administered to humans. Lastly, using a codon adaptation for the E. coli K12 bacterium produced optimum GC content and a high CAI value, which was followed by in silico integration into a pET28 b (+) cloning vector. Conclusions: Generally, these results propose that the design of an epitope-based subunit vaccine can function as an outstanding preventive vaccine candidate against HEV, although validation techniques via in vitro and in vivo approaches are required to justify this statement. Full article
(This article belongs to the Special Issue Editor's Choice Series for the Computational Biology and Medicine Section)
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13 pages, 1558 KiB  
Article
ConsensusPrime—A Bioinformatic Pipeline for Efficient Consensus Primer Design—Detection of Various Resistance and Virulence Factors in MRSA—A Case Study
by Maximilian Collatz, Martin Reinicke, Celia Diezel, Sascha D. Braun, Stefan Monecke, Annett Reissig and Ralf Ehricht
BioMedInformatics 2024, 4(2), 1249-1261; https://doi.org/10.3390/biomedinformatics4020068 - 10 May 2024
Viewed by 837
Abstract
Background: The effectiveness and reliability of diagnostic tests that detect DNA sequences largely hinge on the quality of the used primers and probes. This importance is especially evident when considering the specific sample being analyzed, as it affects the molecular background and potential [...] Read more.
Background: The effectiveness and reliability of diagnostic tests that detect DNA sequences largely hinge on the quality of the used primers and probes. This importance is especially evident when considering the specific sample being analyzed, as it affects the molecular background and potential for cross-reactivity, ultimately determining the test’s performance. Methods: Predicting primers based on the consensus sequence of the target has multiple advantages, including high specificity, diagnostic reliability, broad applicability, and long-term validity. Automated curation of the input sequences ensures high-quality primers and probes. Results: Here, we present a use case for developing a set of consensus primers and probes to identify antibiotic resistance and virulence genes in Staphylococcus (S.) aureus using the ConsensusPrime pipeline. Extensive qPCR experiments with several S. aureus strains confirm the exceptional quality of the primers designed using the pipeline. Conclusions: By improving the quality of the input sequences and using the consensus sequence as a basis, the ConsensusPrime pipeline pipeline ensures high-quality primers and probes, which should be the basis of molecular assays. Full article
(This article belongs to the Special Issue Editor's Choice Series for the Computational Biology and Medicine Section)
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Review

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24 pages, 566 KiB  
Review
Recent Computational Approaches in Understanding the Links between Molecular Stress and Cancer Metastasis
by Eugenia Papadaki, Petros Paplomatas, Panagiotis Vlamos and Aristidis G. Vrahatis
BioMedInformatics 2024, 4(3), 1783-1806; https://doi.org/10.3390/biomedinformatics4030098 - 31 Jul 2024
Viewed by 285
Abstract
In the modern era of medicine, advancements in data science and biomedical technologies have revolutionized our understanding of diseases. Cancer, being a complex disease, has particularly benefited from the wealth of molecular data available, which can now be analyzed using cutting-edge artificial intelligence [...] Read more.
In the modern era of medicine, advancements in data science and biomedical technologies have revolutionized our understanding of diseases. Cancer, being a complex disease, has particularly benefited from the wealth of molecular data available, which can now be analyzed using cutting-edge artificial intelligence (AI) and information science methods. In this context, recent studies have increasingly recognized chronic stress as a significant factor in cancer progression. Utilizing computational methods to address this matter has demonstrated encouraging advancements, providing a hopeful outlook in our efforts to combat cancer. This review focuses on recent computational approaches in understanding the molecular links between stress and cancer metastasis. Specifically, we explore the utilization of single-cell data, an innovative technique in DNA sequencing that allows for detailed analysis. Additionally, we explore the application of AI and data mining techniques to these complex and large-scale datasets. Our findings underscore the potential of these computational pipelines to unravel the intricate relationship between stress and cancer metastasis. However, it is important to note that this field is still in its early stages, and we anticipate a proliferation of similar approaches in the near future, further advancing our understanding and treatment of cancer. Full article
(This article belongs to the Special Issue Editor's Choice Series for the Computational Biology and Medicine Section)
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