Research & Reviews : Journal of Computational Biology

Research and Reviews : Journal of Computational Biology [2319-3433(e)] is a peer-reviewed hybrid open-access journal launched in 2012 focused on the publication of current research work carried out under computational Biology. This journal covers all major fields of applications in Computational Biology.

The journal welcomes contributions across the following topic areas:

• Bioinformatics: genome sequence analysis and annotation, gene variation interpretation, gene expression profiling, single-cell data analysis, comparative genomics, whole-genome sequencing, and functional annotation pipelines
• Computational & Statistical Genetics: genome evolution analysis, DNA and RNA sequence analysis, microRNA and long non-coding RNA identification, statistical genetics methodologies, biostatistics, and genome-wide association studies (GWAS)
• Genomics: functional genomics and comparative genomics, single-nucleotide polymorphism (SNP) analysis, diagnostic and pharmacogenomic testing, structural genomics, and genomic data interpretation
• Proteomics: protein structure prediction and analysis, functional and structural proteomics, mass spectrometry data analysis, protein-protein interaction mapping, and biomarker discovery from proteomic data
• Metabolomics & Metagenomic Analysis: metabolic pathway analysis, large-scale omics data integration, microbial community composition analysis, and multi-omics integration methodologies
• Mathematical Biology: mathematical modeling of biological systems, differential equations for biological processes, dynamical systems analysis, graph theory applications, and quantitative systems biology
• Medical Informatics & Clinical Bioinformatics: electronic health records analysis, clinical decision support systems, biomarker-based patient stratification, and computational epidemiology
• Computational Drug Design: structure-based and ligand-based drug design, quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic and pharmacodynamic modeling, and virtual screening
• Computational Biomodeling: systems biology modeling, computational simulations of biological processes, network analysis, protein folding prediction, and pathway modeling
• Biological Algorithms & Machine Learning: genetic algorithms applied to biological optimization, neural network applications in bioinformatics, image processing for biomedical imaging, data clustering and mining, and evolutionary computation methods