Quantitative Biology

Aims & Scope

Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and to gain a deeper understanding of life sciences.

It aims to provide a platform for not only the analysis but also the integration and construction of biological systems.

It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers, in order to promote rapid communications among scientists.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.4
2024	0.60

SJR (SCImago Journal Rank)

Year	Value
2024	0.328

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	24

Journal Rank

Year	Value
2024	15796

Journal Citation Indicator

Year	Value
2024	131

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Biochemistry, Genetics and Molecular Biology, Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Identifying viruses from metagenomic data using deep learning

  Citation: 505

  Authors: Jie, Kai, Chao, Nathan A., Jed A., Yi, Xiaohui, Ryan, Fengzhu

• Progress in molecular docking

  Citation: 449

  Authors: Jiyu, Ailing, Le

• Target specificity of the CRISPR‐Cas9 system

  Citation: 296

  Authors: Xuebing, Andrea J., Phillip A.

• Performance measures in evaluating machine learning based bioinformatics predictors for classifications

  Citation: 252

  Authors: Yasen, Pufeng

• Modeling the epidemic dynamics and control of COVID‐19 outbreak in China

  Citation: 207

  Authors: Shilei, Hua

• Modeling the specificity of protein‐DNA interactions

  Citation: 187

  Authors: Gary D.

• Predicting enhancer‐promoter interaction from genomic sequence with deep neural networks

  Citation: 116

  Authors: Shashank, Yang, Barnabás, Jian

• Current challenges and solutions of <i><b>de novo</b></i> assembly

  Citation: 77

  Authors: Xingyu, Min, You, Fang‐Xiang, Jianxin

• Variable importance‐weighted Random Forests

  Citation: 76

  Authors: Yiyi, Hongyu

• Differential methylation analysis for bisulfite sequencing using DSS

  Citation: 63

  Authors: Hao, Hao

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