Statistical Applications in Genetics and Molecular Biology

Statistical Applications in Genetics and Molecular Biology 由 Walter de Gruyter GmbH 出版，属中科院数学大类（2025 升级版4区）。按 OpenAlex 近年发文统计，该刊主要发表 Gene expression and cancer classification、Bioinformatics and Genomic Networks、Genetic Mapping and Diversity in Plants and Animals 等主题的论文，这 3 个主题约占 40.5%，主导子领域为 Molecular Biology、Genetics。（根据 OpenAlex 主题统计与中科院分区数据生成）

About this journal ObjectiveStatistical Applications in Genetics and Molecular Biology (SAGMB) is a peer reviewed journal dedicated to advancing statistical, machine learning, and artificial intelligence (AI) methodologies and their applications across the full spectrum of modern molecular biosciences. The journal welcomes high-quality research addressing analytical challenges in all omics domains, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics.SAGMB serves as a platform for rigorous quantitative methods that support the analysis and interpretation of complex, high-dimensional biological data. Submissions that introduce novel techniques or demonstrate insightful applications of existing statistical, machine learning, and AI approaches to molecular biology are strongly encouraged. Review papers and Tutorials (including on software applications) are also welcome. Article formats Original Research Articles, Review Articles, Tutorials. Your Benefits Your benefits Theoretical and computational innovations in the statistical analysis of biological data and systems Practical applications of statistical ideas to computational biology, genomics, and systems biology and medicine Specialized expertise in genomics, molecular, cellular and systems biology Internationally recognized authors and editorial board Rapid online publication of contributions as "ahead-of-print" versions High quality peer review Rapid "ahead-of-print" online publication of accepted papers Open Access publication option available Complete Archive AccessAs of 2026, all De Gruyter journal subscriptions include access to every issue, from Volume 1, Issue 1 through to today, meaning subscribers can access even more research at no additional cost. History Content available since 2002 (Volume 1, Issue 1) Previously published by Berkeley Electronic Press Other publications in the field The International Journal of Biostatistics Bio-Algorithms and Med-Systems Journal of Applied Mathematics, Statistics and Informatics

摘自期刊官网 Aims & Scope

研究方向（LetPub）：生物-生化与分子生物学

主编 Shili Lin出版商官网

中科院期刊分区表

数据库收录

SCIEScopusLetPub

国际期刊预警名单：各版本均未列入LetPub

期刊官网自述收录

CASMathSciNetCurrent Contents出版商官网

其他：EBSCO、ProQuest、Baidu、AGRICOLA、OCLC

期刊官网列出的收录清单，未与各数据库逐一核对；Web of Science 各子库以上方记录为准。

不同版本、大类与小类分别展示，不将不同评价口径合并为一个分区值；投稿与考核前请确认所在机构采用的版本。

影响力指标

影响因子

0.75LetPub · 2026-09-13

影响因子（出版商公布）

0.8出版商官网 · 2026-09-29

五年影响因子（出版商公布）

0.7出版商官网 · 2026-09-29

CiteScore 2026-06

1.2LetPub · 2026-09-13

CiteScore（出版商公布）

1.2出版商官网 · 2026-09-29

SJR 2026-06

0.23LetPub · 2026-09-13

SNIP 2026-06

0.34LetPub · 2026-09-13

h-index

41LetPub · 2026-09-13

h-index

66OpenAlex · 2026-10-01

i10-index

331OpenAlex · 2026-10-01

两年平均被引

1.05OpenAlex · 2026-10-01

自引率

12.5%LetPub · 2026-09-13

Gold OA 文章占比

36.36%LetPub · 2026-09-13

发文规模

年文章数

2LetPub · 2026-09-13

不同来源的统计口径与观测时间不同，页面保留各来源原值；“出版商公布”为期刊官网自述数据。

其他来源

平均审稿速度（社区反馈）

较慢,6-12周LetPub · 2026-09-13

平均录用比例（社区反馈）

较易LetPub · 2026-09-13

社区反馈来自投稿者样本，置信度 0.5，不代表期刊承诺；“出版商公布”的时间节点定义以官网为准。

混合期刊的文章处理费仅在作者选择开放获取发表时收取；按订阅方式发表通常不收费。

费用以期刊官网为准；不同来源的观测时间可能不同，页面保留各来源原值，不做平均。

摘录来自期刊官网页面，仅保留原文片段；“模型摘要”由 Workers AI 生成并经原文校验，以官网为准。

出版商

Walter de Gruyter GmbH

出版国家或地区

UNITED STATES

出版语言

English

出版周期

Irregular

创刊年份

2002

ISSN

1544-6115

印刷版 ISSN

2194-6302

官方链接

• 期刊主页 ↗LetPub
• 投稿系统 ↗LetPub
• 专刊 / 特刊征稿 ↗出版商官网