Shirima, C.A., Bleotu, C., Spandidos, D.A., El-Naggar, A.K., Gradisteanu Pircalabioru, G., and Michalopoulos, I. (2024). Epithelial-derived head and neck squamous tumourigenesis (Review). Oncol Rep 52, 141.
Special Issue “Advances in Computational Biology and Bioinformatics”
Guest Editors
Georgios Pavlopoulos
Research Director, Institute for Fundamental Biomedical Research
BSRC Alexander Fleming, Greece
Email: pavlopoulos@fleming.gr
Nikos Kyrpides
Microbiome Data Science Group Lead, DOE Joint Genome Institute
Lawrence Berkeley National Laboratory, United States of America
Email: nckyrpides@lbl.gov
Ιoannis Michalopoulos
Staff Research Scientist, Centre of Systems Biology
Biomedical Research Foundation of the Academy of Athens, Greece
Email: imichalop@bioacademy.gr
Fotis Baltoumas
Postdoctoral Researcher, Bioinformatics and Integrative Biology Lab
BSRC Alexander Fleming, Greece
Email: baltoumas@fleming.gr
Evangelos Karatzas
Research Fellow, ARISE
European Bioinformatics Institute, United Kingdom
Email: vangelis@ebi.ac.uk
The rapid advancements in computational biology and bioinformatics have revolutionized our understanding of biological systems, providing unprecedented insights into the complexities of life. This special issue, titled “Advances in Computational Biology and Bioinformatics,” aims to highlight the cutting-edge research and innovative methodologies propelling the field forward varying from the development of sophisticated algorithms for data analysis to the integration of multi-omics approaches, computational biology and bioinformatics. These advances not only enhance our ability to decode the vast amounts of data generated by modern experimental techniques but also pave the way for new discoveries in genomics, proteomics, systems biology, and personalized medicine. By showcasing the latest breakthroughs and emerging trends, this issue seeks to underscore the pivotal role of computational approaches in addressing some of the most pressing challenges in biology and medicine today.
Scope of the Special Issue:
The main scope of the special issue on “Advances in Computational Biology and Bioinformatics” encompasses a wide array of topics that reflect the dynamic and interdisciplinary nature of the field. This issue aims to cover (but not limited) the following key areas:
- Algorithm Development and Data Analysis: Papers in this section will focus on novel algorithms, computational models, and analytical tools designed to process and interpret biological data. This includes advancements in machine learning, artificial intelligence, and statistical methods that enhance the accuracy and efficiency of data analysis.
- Genomics and Transcriptomics: Contributions will explore computational techniques applied to genomic and transcriptomic data. This includes genome assembly, annotation, variant analysis, and the interpretation of gene expression patterns, as well as the integration of genomic data with other biological datasets.
- Proteomics and Metabolomics: This area will highlight computational approaches in the study of proteins and metabolites. Topics may include protein structure prediction, protein-protein interactions, metabolite profiling, and the integration of proteomic and metabolomic data for comprehensive biological insights.
- Systems Biology: Papers will delve into the modelling and simulation of complex biological systems. This includes network biology, dynamic systems modelling, and the use of computational tools to understand cellular pathways, interactions, and regulatory mechanisms.
- Structural Biology and Molecular Dynamics: This section will cover computational methods in structural biology, including molecular dynamics simulations, docking studies, and the prediction of biomolecular structures and functions.
- Bioinformatics in Personalized Medicine: Contributions will explore how computational biology is advancing personalized medicine. Topics may include the analysis of patient-specific data, biomarker discovery, drug repositioning, and the development of tailored therapeutic strategies.
- Big Data and High-Performance Computing: Papers will focus on the challenges and solutions related to handling and analyzing large-scale biological data. This includes the use of high-performance computing, cloud-based platforms, and data storage solutions to manage the vast amounts of data generated in modern biological research.
- Emerging Trends and Future Directions: This section will provide insights into the latest trends and future directions in computational biology and bioinformatics. Topics may include emerging technologies, interdisciplinary approaches, and the potential impact of these advancements on various fields of biology and medicine.
By addressing these diverse yet interconnected areas, this special issue aims to provide a comprehensive overview of the current state of computational biology and bioinformatics, highlighting the theoretical advancements and practical applications driving the field forward.
All manuscripts will receive expedited handling and the accepted version will appear online within one week. Please note that every accepted paper is subjected to a processing fee as per Elsevier’s open access journal policies.
If you have any further questions, please feel free to contact us at
publications.assistant@csbj-rncsb.org
A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19
Christodoulou, A., Katsarou, M.S., Emmanouil, C., Gavrielatos, M., Georgiou, D., Tsolakou, A., Papasavva, M., Economou, V., Nanou, V., Nikolopoulos, I., Daganou, M., Argyraki, A., Stefanidis, E., Metaxas, G., Panagiotou, E., Michalopoulos, I., and Drakoulis, N. (2024). A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19. BioTech 13, 22.
Genes encoding γ‑glutamyl‑transpeptidases in the allicin biosynthetic pathway in garlic (Allium sativum)
Baltzi, E., Papaloukas, C., Spandidos, D.A., and Michalopoulos, I. (2024). Genes encoding γ‑glutamyl‑transpeptidases in the allicin biosynthetic pathway in garlic (Allium sativum). Biomed Rep 20, 45.
Differential Gene Expression in Human Fibroblasts Simultaneously Exposed to Ionizing Radiation and Simulated Microgravity
Malatesta, P., Kyriakidis, K., Hada, M., Ikeda, H., Takahashi, A., Saganti, P.B., Georgakilas, A.G., and Michalopoulos, I. (2024). Differential Gene Expression in Human Fibroblasts Simultaneously Exposed to Ionizing Radiation and Simulated Microgravity. Biomolecules 14, 88.
miRNA-Based Technologies in Cancer Therapy
Pagoni, M., Cava, C., Sideris, D.C., Avgeris, M., Zoumpourlis, V., Michalopoulos, I., and Drakoulis N. (2023). miRNA-Based Technologies in Cancer Therapy. J Pers Med 13, 1586.
Special Issue on Differential Gene Expression and Coexpression
Zogopoulos, V.L., Malatras, A., and Michalopoulos, I. (2023). Special Issue on Differential Gene Expression and Coexpression. Biology 12, 1226.
The Stem Cell Expression Profile of Odontogenic Tumors and Cysts: A Systematic Review and Meta-Analysis
Kalogirou, E.M., Lekakis, G., Petroulias, A., Chavdoulas, K., Zogopoulos, V.L., Michalopoulos, I., and Tosios, K.I. (2023). The Stem Cell Expression Profile of Odontogenic Tumors and Cysts: A Systematic Review and Meta-Analysis. Genes 14, 1735.
Special Issue “Differential Gene Expression and Coexpression 2.0”
Dr Ioannis Michalopoulos and Dr Apostolos Malatras are Guest Editors of Special Issue “Differential Gene Expression and Coexpression 2.0” of Biology (Impact Factor 3.6, JCR category rank: Q1: Biology). Deadline for manuscript submissions: 30 June 2024.
The most common approach in transcriptomics (RNA-seq and microarrays) is differential gene expression analysis. Genes identified as differentially expressed may be responsible for phenotype differences between various biological conditions. An alternative approach is gene co-expression analysis, which detects groups of genes with similar expression patterns across unrelated sets of transcriptomic data from the same organism. Co-expressed genes tend to be involved in similar biological processes. This Special Issue will include reviews and research articles on the topic of differential gene expression and coexpression. The reviews will provide an overview of the methods available for transcriptomic analysis, while the research articles will provide an in-depth description of each state-of-the-art tool. Please send me an abstract prior to submission to make sure that your work falls within the scope of this Special Issue.
Advances in Bioinformatics and Computational Biology of Human Disease
Dr. Ioannis Michalopoulos and Dr. Georgios A. Pavlopoulos are editors to “Advances in Bioinformatics and Computational Biology of Human Disease” topic
Abstract submission deadline
31 October 2023
Manuscript submission deadline
31 December 2023
In today’s big-data era, the exponential growth of information due to the latest advancements in high-throughput technologies is indisputable. Therefore, efficient algorithms and tools for the extraction, analysis, exploration, and representation of biological information are necessary. In this regard, we invite investigators to contribute original bioinformatics research and review articles describing novel methods, algorithms, software applications, web services, and workflows that are able to cope with larger datasets, complexity, and new datasets or databases which integrate information from different sources. Submissions across the entire spectrum of life and biomedical sciences are welcomed.
Keywords
- genomics
- sequence analysis
- gene expression
- structural bioinformatics
- gene regulation
- proteomics
- metabolomics
- biological networks
- data visualization
- data integration
- AI/ML
- personalized medicine
- meta-analysis
- cancer research
- disease research