Department of Bioinformatics

Aims and Scope

The Department of Bioinformatics of The Gazette of Medical Sciences (The GMS) promotes the development and application of computational approaches that transform biological data into meaningful scientific knowledge. Bioinformatics is essential to contemporary research because biological systems generate information at scales that cannot be adequately interpreted through conventional experimental methods alone. The Department publishes peer-reviewed, open-access research connecting computational methodology with biological discovery, disease mechanisms and translational medicine. Its intended readership includes biologists seeking computational insights and computer scientists developing methods for biological and biomedical applications.

The Department welcomes research on biological sequence analysis, genome assembly, sequence alignment, genome annotation, gene prediction, comparative genomics, evolutionary bioinformatics and the computational interpretation of genetic variation. Contributions may investigate gene expression, transcriptomics, proteomics, metabolomics, epigenomics and the integration of multiple omics datasets. Particular interest is given to analytical strategies that identify biologically meaningful patterns, explain molecular processes or generate testable hypotheses. Studies introducing new algorithms, statistical approaches or computational workflows should demonstrate their value through appropriate biological applications, benchmarking or validation.

Understanding the organization and behavior of biological networks represents another major area of interest. The Department encourages investigations involving gene regulatory networks, protein–protein interaction networks, co-expression analysis, functional enrichment, pathway modeling and systems biology. Structural bioinformatics, including protein structure prediction, molecular modeling, protein–ligand interactions, structural annotation and computational analysis of macromolecular complexes, is also within scope. Research should explain how computational results contribute to biological understanding rather than presenting predictions without scientific interpretation.

Clinical and translational bioinformatics are particularly relevant to the Department's mission. Contributions may apply computational methods to cancer genomics, cardiovascular disease, infectious diseases, immunology, neurological disorders, rare diseases and other biomedical research areas. Topics include biomarker discovery, molecular disease classification, immune cell deconvolution, drug-target identification, pharmacogenomics, single-cell analysis and computational approaches to precision medicine. The Department welcomes machine learning and artificial intelligence studies when the methods are rigorously evaluated and their biological or clinical significance is appropriately supported. Computational associations and predicted biomarkers should not be presented as clinically validated findings without independent evidence.

Reliable biological data analysis depends on transparent methodology, appropriate statistical inference and reproducible computational practice. The Department therefore supports research on biological databases, data annotation, data integration, software tools, algorithm benchmarking, workflow development, high-performance computing, data visualization and research reproducibility. Original research articles, methodological contributions, reviews, rapid communications, expert commentaries, case-based computational investigations, letters and scholarly perspectives are considered. Submissions should clearly describe their datasets, analytical methods, limitations and contribution to biological knowledge. Interdisciplinary collaboration between computational researchers, laboratory scientists and clinicians is strongly encouraged.

Areas of Interest

• Bioinformatics
• Computational Biology
• Analysis and Annotation of Biological Structures
• Bioinformatics and Biological Discovery
• Biological Sequence Analysis
• DNA and RNA Sequence Alignment
• Genome Assembly
• Genome Annotation
• Gene Prediction
• Comparative Genomics
• Evolutionary Bioinformatics
• Phylogenetic Analysis
• Population Genomics
• Genetic Variant Analysis
• Functional Genomics
• Transcriptomics
• RNA Sequencing Data Analysis
• Single-Cell RNA Sequencing
• Spatial Transcriptomics
• Proteomics Data Analysis
• Metabolomics Data Analysis
• Epigenomics
• Multi-Omics Integration
• Gene Expression Profiling
• Differential Gene Expression
• Gene Co-Expression Networks
• Weighted Gene Co-Expression Analysis
• Gene Regulatory Networks
• Protein–Protein Interaction Networks
• Network Biology
• Systems Biology
• Pathway Analysis
• Functional Enrichment Analysis
• Gene Ontology Analysis
• Gene Set Enrichment Analysis
• Biological Network Visualization
• Structural Bioinformatics
• Protein Structure Prediction
• Protein Structure Annotation
• Protein–Ligand Interactions
• Molecular Docking
• Molecular Dynamics Simulation
• Computational Structural Biology
• Biological Database Development
• Biomedical Data Integration
• Biological Data Mining
• Computational Statistics in Biology
• Biostatistical Modeling
• Machine Learning in Bioinformatics
• Artificial Intelligence in Biology
• Deep Learning for Biological Data
• Computational Biomarker Discovery
• Disease-Associated Gene Identification
• Cancer Bioinformatics
• Cardiovascular Bioinformatics
• Neuroinformatics
• Immunoinformatics
• Immune Cell Deconvolution
• Infectious Disease Bioinformatics
• Microbial Genomics
• Metagenomics
• Microbiome Data Analysis
• Viral Genome Analysis
• Clinical Bioinformatics
• Precision Medicine Informatics
• Pharmacogenomics Informatics
• Computational Drug Discovery
• Drug-Target Prediction
• Biomedical Text Mining
• Biomedical Knowledge Graphs
• Biological Data Visualization
• Bioinformatics Software Development
• Algorithm Development and Benchmarking
• Reproducible Computational Workflows
• High-Performance Biological Computing
• Open Biological Data and Databases
• Translational Bioinformatics

The Department particularly encourages studies that bridge computational innovation and biological relevance. All submissions undergo editorial assessment and peer review in accordance with the journal's applicable editorial and publication policies.