PLoS Computational Biology

PLoS Computational Biology
期刊缩写:
PLoS Comput. Biol.
影响因子:
4.3
ISSN:
print: 1553-734X
on-line: 1553-7358
研究领域:
生物-生化研究方法
创刊年份:
2005年
h-index:
148
自引率:
4.70%
Gold OA文章占比:
98.96%
原创研究文献占比:
99.19%
SCI收录类型:
Science Citation Index Expanded (SCIE) || Scopus (CiteScore) || Directory of Open Access Journals (DOAJ)
期刊介绍英文:
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.
CiteScore:
CiteScoreSJRSNIPCiteScore排名
7.11.6521.085
学科
排名
百分位
大类:Mathematics
小类:Modeling and Simulation
32 / 324
90%
大类:Agricultural and Biological Sciences
小类:Ecology, Evolution, Behavior and Systematics
87 / 721
88%
大类:Computer Science
小类:Computational Theory and Mathematics
23 / 176
87%
大类:Environmental Science
小类:Ecology
63 / 461
86%
大类:Biochemistry, Genetics and Molecular Biology
小类:Genetics
97 / 347
72%
大类:Neuroscience
小类:Cellular and Molecular Neuroscience
34 / 97
65%
大类:Biochemistry, Genetics and Molecular Biology
小类:Molecular Biology
163 / 410
60%
发文信息
中科院SCI期刊分区
大类 小类 TOP期刊 综述期刊
2区 生物学
2区 生化研究方法 BIOCHEMICAL RESEARCH METHODS
2区 数学与计算生物学 MATHEMATICAL & COMPUTATIONAL BIOLOGY
WOS期刊分区
历年影响因子
2015年4.5870
2016年4.5420
2017年3.9550
2018年4.4280
2019年4.7000
2020年4.4750
2021年4.7790
2022年4.3000
历年发表
2012年549
2013年642
2014年732
2015年804
2016年960
2017年689
2018年587
2019年711
2020年830
2021年1036
2022年820
投稿信息
出版周期:
Monthly
出版语言:
English
出版国家(地区):
UNITED STATES
出版商:
Public Library of Science
编辑部地址:
PUBLIC LIBRARY SCIENCE, 185 BERRY ST, STE 1300, SAN FRANCISCO, USA, CA, 94107

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Ten simple rules for teaching an introduction to R

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Evolutionary analyses of intrinsically disordered regions reveal widespread signals of conservation.

Pub Date : 2024-04-25 DOI: 10.1371/journal.pcbi.1012028 Marc D Singleton, Michael B. Eisen
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