Statistical Applications in Genetics and Molecular Biology

Aims & Scope

Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology.

The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered.

The range of topics is wide and includes topics such as linkage mapping, association studies, gene regulatory networks, protein structure prediction, high-throughput data analysis, molecular evolution and phylogenetic trees, multi-omics data integration, as well as genetic and epigenetic data modelling.

The journal publishes Original Research Articles, Review Articles, Software Application Notes, and Book Reviews.

Before submitting Review Articles please contact the Editor-in-Chief to discuss remit and content of the proposed review.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.4
2024	0.80

SJR (SCImago Journal Rank)

Year	Value
2024	0.213

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	53

Journal Rank

Year	Value
2024	20454

Journal Citation Indicator

Year	Value
2024	18

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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
• Medline
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Biochemistry, Genetics and Molecular Biology 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.

• A General Framework for Weighted Gene Co-Expression Network Analysis

  Citation: 4375

  Authors: Bin, Steve

• Super Learner

  Citation: 1315

  Authors: Mark J., Eric C, Alan E.

• A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics

  Citation: 1032

  Authors: Juliane, Korbinian

• Random Forests for Genetic Association Studies

  Citation: 197

  Authors: Benjamin A, Eric C, Farren B. S.

• Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge

  Citation: 168

  Authors: Adriano V, Dirk

• Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach

  Citation: 124

  Authors: Rainer, Korbinian

• High-Dimensional Regression and Variable Selection Using CAR Scores

  Citation: 114

  Authors: Verena, Korbinian

• Approximate Bayesian computation (ABC) gives exact results under the assumption of model error

  Citation: 113

  Authors: Richard David

• On the Operational Characteristics of the Benjamini and Hochberg False Discovery Rate Procedure

  Citation: 109

  Authors: Gerwyn H, Peter J.

• The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

  Citation: 102

  Authors: Yanming, Daniel W, Jason S, Jeff H

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