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Plos Computational BiologySCIE

国际简称：PLOS COMPUT BIOL 参考译名：Plos 计算生物学

中科院分区
2区
CiteScore分区
Q1
JCR分区
Q1

基本信息：: ISSN：1553-7358; E-ISSN：1553-7358; 是否OA：开放; 是否预警：否; TOP期刊：是

出版信息：: 出版地区：United States; 出版商：Public Library of Science; 出版语言：English; 出版周期：Monthly; 出版年份：2005; 研究方向：Environmental Science-Ecology

评价信息：: 影响因子：3.8; H-index：138; CiteScore指数：7.1; SJR指数：1.652; SNIP指数：1.085

发文数据：: Gold OA文章占比：99.67%; 研究类文章占比：98.90%; 年发文量：637; 自引率：0.0465...; 开源占比：0.9896; 出版撤稿占比：0; 出版国人文章占比：0.04; OA被引用占比：1

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英文简介期刊介绍 CiteScore数据中科院SCI分区 JCR分区发文数据常见问题

英文简介Plos Computational Biology期刊介绍

PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.

Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines.

Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights.

Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology.

Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.

期刊简介Plos Computational Biology期刊介绍

《Plos Computational Biology》自2005出版以来，是一本生物学优秀杂志。致力于发表原创科学研究结果，并为生物学各个领域的原创研究提供一个展示平台，以促进生物学领域的的进步。该刊鼓励先进的、清晰的阐述，从广泛的视角提供当前感兴趣的研究主题的新见解，或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道生物学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单，目前已被权威数据库SCIE收录，得到了广泛的认可。

该期刊投稿重要关注点：

预计审稿时间： 32 Weeks
国际TOP期刊
生物学
BIOCHEMICAL RESEARCH METHODS
SCIE
中科院2区
非预警

Cite Score数据（2024年最新版）Plos Computational Biology Cite Score数据

CiteScore：7.1
SJR：1.652
SNIP：1.085

学科类别	分区	排名	百分位
大类：Mathematics 小类：Modeling and Simulation	Q1	32 / 324	90%
大类：Mathematics 小类：Ecology, Evolution, Behavior and Systematics	Q1	87 / 721	88%
大类：Mathematics 小类：Computational Theory and Mathematics	Q1	23 / 176	87%
大类：Mathematics 小类：Ecology	Q1	63 / 461	86%
大类：Mathematics 小类：Genetics	Q2	97 / 347	72%
大类：Mathematics 小类：Cellular and Molecular Neuroscience	Q2	34 / 97	65%
大类：Mathematics 小类：Molecular Biology	Q2	163 / 410	60%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础，针对的是前四年发表的论文的引文。CiteScore的意义在于，它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法，而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Plos Computational Biology 中科院分区

中科院 2023年12月升级版 综述期刊：否 Top期刊：否

大类学科	分区	小类学科	分区
生物学	2区	BIOCHEMICAL RESEARCH METHODS 生化研究方法 MATHEMATICAL & COMPUTATIONAL BIOLOGY 数学与计算生物学	2区 2区

中科院分区表 是以客观数据为基础，运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据，得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次，类似于“优、良、及格”等。最开始，这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Plos Computational Biology JCR分区

2023-2024 年最新版

按JIF指标学科分区	收录子集	分区	排名	百分位
学科：BIOCHEMICAL RESEARCH METHODS	SCIE	Q1	15 / 85	82.9%
学科：MATHEMATICAL & COMPUTATIONAL BIOLOGY	SCIE	Q1	11 / 65	83.8%

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：BIOCHEMICAL RESEARCH METHODS	SCIE	Q1	15 / 85	82.94%
学科：MATHEMATICAL & COMPUTATIONAL BIOLOGY	SCIE	Q1	12 / 65	82.31%

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异，此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此，JCR将期刊按照学科门类和影响因子分为不同的分区，这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

发文数据

2023-2024 年国家/地区发文量统计

国家/地区数量
USA1072
England323
GERMANY (FED REP GER)284
France170
CHINA MAINLAND125
Canada123
Switzerland113
Spain99
Netherlands91
Australia85

本刊中国学者近年发表论文

1、SurvivalPath:A R package for conducting personalized survival path mapping based on time-series survival data
Author: Shen, Lujun; Mo, Jinqing; Yang, Changsheng; Jiang, Yiquan; Ke, Liangru; Hou, Dan; Yan, Jingdong; Zhang, Tao; Fan, Weijun

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 1, pp. -. DOI: 10.1371/journal.pcbi.1010830
2、A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions
Author: Ma, Mei; Lei, Xiujuan

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 1, pp. -. DOI: 10.1371/journal.pcbi.1010812
3、HiSV: A control-free method for structural variation detection from Hi-C data
Author: Li, Junping; Gao, Lin; Ye, Yusen

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 1, pp. -. DOI: 10.1371/journal.pcbi.1010760
4、A new model of Notch signalling: Control of Notch receptor cis-inhibition via Notch ligand dimers
Author: Chen, Daipeng M.; Forghany, Zary; Liu, Xinxin M.; Wang, Haijiang; Merks, Roeland M. H. M.; Baker, David

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 1, pp. -. DOI: 10.1371/journal.pcbi.1010169
5、MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders
Author: Zhang, Peng; Tu, Shikui

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 3, pp. -. DOI: 10.1371/journal.pcbi.1010951
6、PCB: A pseudotemporal causality-based Bayesian approach to identify EMT-associated regulatory relationships of AS events and RBPs during breast cancer progression
Author: Sun, Liangjie; Qiu, Yushan; Ching, Wai-Ki; Zhao, Pu; Zou, Quan

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 3, pp. -. DOI: 10.1371/journal.pcbi.1010939
7、Bioinspired figure-ground discrimination via visual motion smoothing
Author: Wu, Zhihua; Guo, Aike

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 4, pp. -. DOI: 10.1371/journal.pcbi.1011077
8、Diverse role of NMDA receptors for dendritic integration of neural dynamics
Author: Tang, Yuanhong; Zhang, Xingyu; An, Lingling; Yu, Zhaofei; Liu, Jian K.

Journal: PLOS COMPUTATIONAL BIOLOGY. 2023; Vol. 19, Issue 4, pp. -. DOI: 10.1371/journal.pcbi.1011019