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BioMedInformatics
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Features of Bioinformatic Analyses for SARS-CoV-2 Infections and Vaccination
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Features of Bioinformatic Analyses for SARS-CoV-2 Infections and Vaccination

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Clinical Informatics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5070

Special Issue Editor

Dr. Yasunari Matsuzaka

E-Mail Website
Guest Editor
Division of Molecular and Medical Genetics, Center for Gene and Cell Therapy, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
Interests: extracellular vesicles; vaccine; cancer; mRNA; microRNAs; immune responses; T cells; dendritic cells; major histocompatibility complex (MHC); deep learning; virus
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an SARS-CoV-2 is an enveloped virus that requires membrane fusion, which is induced through a two-step, conformational change by the binding of the viral spike protein to the cell-side receptor and proteases between the cell membrane and virus membrane for cell entry. Two different SARS-CoV-2 pathways known to entry cells are through late endosomes and cell surfaces. Currently, the mRNA vaccine against SARS-CoV-2 has been developed based on the genetic information of the virus, , which is different from conventional live attenuated and inactive vaccines. As the first step in mRNA vaccines, mRNA is taken up by macrophages in tissues near the injection site; after that, the spike protein synthesized in the cytoplasm is presented on the surface of the macrophages, which evokes an immune response that includes antibody production.         

This infectious disease (COVID-19) caused by the novel coronavirus (SARS-CoV-2) is still spreading worldwide through mutant strains. In order to deal with this unprecedented situation, therapeutic drugs and vaccines against COVID-19 are being commercialized faster than ever before. Along with changes in infectivity, transmissibility, antigenicity, and pathogenicity, the efficacy of current vaccines is also of concern in the emergence of mutant strains. Multiple vaccines of different types are currently licensed, including mRNA vaccines, viral vector vaccines, and recombinant protein vaccines. At present, the following factors have been clarified regarding the preventive effects obtained by vaccines and their mechanisms of action. Neutralizing antibodies against the S protein play an important role in the protective effects induced by commercial vaccines. It is possible that effects other than the neutralizing activity of cell-mediated immunity and humoral immunity also contribute to the preventive effect of vaccines, and these immune responses may affect the long-term persistence of vaccine efficacy and preventive effects against severe disease. Various types of immune responses contribute to the protective efficacy of vaccines, while the contribution of the neutralizing antibodies produced in serum varies by vaccine type. However, the threshold level of neutralizing antibodies in serum that reliably predicts the prevention of onset and severity of the disease has not been clarified.

This Special Issue aims to highlight the latest research on the efficacy, development, molecular mechanisms, or prevention of SARS-CoV-2 infection. The topics that we intend to cover include the following (but are not limited to):

  • Artificial intelligence;
  • Bioinformatics;
  • Data informatics;
  • Machine learning;
  • Statistical computing;
  • QSAR;
  • SARS-CoV-2;
  • Vaccines.

We welcome both research and review articles.

We look forward to receiving your contributions.

Dr. Yasunari Matsuzaka
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

  • artificial intelligence
  • bioinformatics
  • data informatics
  • machine learning
  • statistical computing
  • QSAR
  • SARS-CoV-2
  • vaccines

Published Papers (3 papers)

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Research

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30 pages, 7526 KiB  
Article
An Efficient COVID-19 Mortality Risk Prediction Model Using Deep Synthetic Minority Oversampling Technique and Convolution Neural Networks
by Rajkumar Soundrapandiyan, Adhiyaman Manickam, Moulay Akhloufi, Yarlagadda Vishnu Srinivasa Murthy, Renuka Devi Meenakshi Sundaram and Sivasubramanian Thirugnanasambandam
BioMedInformatics 2023, 3(2), 339-368; https://doi.org/10.3390/biomedinformatics3020023 - 05 May 2023
Cited by 1 | Viewed by 2105
Abstract
The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, the COVID-19 virus has not completely vanished from the world yet, and thus, global agitation is still increasing with mutations and variants of [...] Read more.
The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, the COVID-19 virus has not completely vanished from the world yet, and thus, global agitation is still increasing with mutations and variants of the same. Early diagnosis is the best way to decline the mortality risk associated with it. This urges the necessity of developing new computational approaches that can analyze a large dataset and predict the disease in time. Currently, automated virus diagnosis is a major area of research for accurate and timely predictions. Artificial intelligent (AI)-based techniques such as machine learning (ML) and deep learning (DL) can be deployed for this purpose. In this, compared to traditional machine learning techniques, deep Learning approaches show prominent results. Yet it still requires optimization in terms of complex space problems. To address this issue, the proposed method combines deep learning predictive models such as convolutional neural network (CNN), long short-term memory (LSTM), auto-encoder (AE), cross-validation (CV), and synthetic minority oversampling techniques (SMOTE). This method proposes six different combinations of deep learning forecasting models such as CV-CNN, CV-LSTM+CNN, IMG-CNN, AE+CV-CNN, SMOTE-CV-LSTM, and SMOTE-CV-CNN. The performance of each model is evaluated using various metrics on the standard dataset that is approved by The Montefiore Medical Center/Albert Einstein College of Medicine Institutional Review Board. The experimental results show that the SMOTE-CV-CNN model outperforms the other models by achieving an accuracy of 98.29%. Moreover, the proposed SMOTE-CV-CNN model has been compared to existing mortality risk prediction methods based on both machine learning (ML) and deep learning (DL), and has demonstrated superior accuracy. Based on the experimental analysis, it can be inferred that the proposed SMOTE-CV-CNN model has the ability to effectively predict mortality related to COVID-19. Full article
(This article belongs to the Special Issue Features of Bioinformatic Analyses for SARS-CoV-2 Infections and Vaccination)
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Review

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25 pages, 3126 KiB  
Review
Unravelling Insights into the Evolution and Management of SARS-CoV-2
by Aganze Gloire-Aimé Mushebenge, Samuel Chima Ugbaja, Nonkululeko Avril Mbatha, Rene B. Khan and Hezekiel M. Kumalo
BioMedInformatics 2024, 4(1), 385-409; https://doi.org/10.3390/biomedinformatics4010022 - 03 Feb 2024
Viewed by 1124
Abstract
Worldwide, the COVID-19 pandemic, caused by the brand-new coronavirus SARS-CoV-2, has claimed a sizable number of lives. The virus’ rapid spread and impact on every facet of human existence necessitate a continuous and dynamic examination of its biology and management. Despite this urgency, [...] Read more.
Worldwide, the COVID-19 pandemic, caused by the brand-new coronavirus SARS-CoV-2, has claimed a sizable number of lives. The virus’ rapid spread and impact on every facet of human existence necessitate a continuous and dynamic examination of its biology and management. Despite this urgency, COVID-19 does not currently have any particular antiviral treatments. As a result, scientists are concentrating on repurposing existing antiviral medications or creating brand-new ones. This comprehensive review seeks to provide an in-depth exploration of our current understanding of SARS-CoV-2, starting with an analysis of its prevalence, pathology, and evolutionary trends. In doing so, the review aims to clarify the complex network of factors that have contributed to the varying case fatality rates observed in different geographic areas. In this work, we explore the complex world of SARS-CoV-2 mutations and their implications for vaccine efficacy and therapeutic interventions. The dynamic viral landscape of the pandemic poses a significant challenge, leading scientists to investigate the genetic foundations of the virus and the mechanisms underlying these genetic alterations. Numerous hypotheses have been proposed as the pandemic has developed, covering various subjects like the selection pressures driving mutation, the possibility of vaccine escape, and the consequences for clinical therapy. Furthermore, this review will shed light on current clinical trials investigating novel medicines and vaccine development, including the promising field of drug repurposing, providing a window into the changing field of treatment approaches. This study provides a comprehensive understanding of the virus by compiling the huge and evolving body of knowledge on SARS-CoV-2, highlighting its complexities and implications for public health, and igniting additional investigation into the control of this unprecedented global health disaster. Full article
(This article belongs to the Special Issue Features of Bioinformatic Analyses for SARS-CoV-2 Infections and Vaccination)
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23 pages, 1140 KiB  
Review
Deep Learning and Federated Learning for Screening COVID-19: A Review
by M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder and Joarder Kamruzzaman
BioMedInformatics 2023, 3(3), 691-713; https://doi.org/10.3390/biomedinformatics3030045 - 01 Sep 2023
Cited by 2 | Viewed by 1305
Abstract
Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between [...] Read more.
Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. Full article
(This article belongs to the Special Issue Features of Bioinformatic Analyses for SARS-CoV-2 Infections and Vaccination)
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