Quantitative Biology focuses on the application of computational and mathematical approaches to biological systems. This includes developing and applying machine learning algorithms for biological data analysis, such as image analysis in developmental biology and cytopathology, and for inferring cellular composition from transcriptomic data. The journal also covers advancements in DNA assembly for synthetic biology and metabolic engineering, and the characterization and engineering of repetitive DNA sequences. Furthermore, it explores quantitative modeling of biological processes, including disease dynamics, protein structure prediction, and genome organization, often integrating data-driven methods with physical or biological laws.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Applied MathematicsQ3
Biochemistry, Genetics and Molecular Biology (miscellaneous)Q3
Computer Science ApplicationsQ3
Modeling and SimulationQ3
Subject Classification
Web of Science Categories
Mathematical & Computational Biology
Scopus Categories
Applied MathematicsComputer Science ApplicationsBiochemistry, Genetics and Molecular Biology (miscellaneous)Modeling and Simulation
Research Topics (OpenAlex)
Gene Regulatory Network AnalysisBioinformatics and Genomic NetworksSingle-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesComputational Drug Discovery MethodsRNA and protein synthesis mechanismsGene expression and cancer classificationGenetics, Bioinformatics, and Biomedical ResearchGenomics and Chromatin DynamicsRNA Research and Splicing