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Plos Computational BiologySCIE

國際簡稱：PLOS COMPUT BIOL 參考譯名：Plos 計(jì)算生物學(xué)

中科院分區(qū)
2區(qū)
CiteScore分區(qū)
Q1
JCR分區(qū)
Q1

基本信息：: ISSN：1553-7358; E-ISSN：1553-7358; 是否OA：開放; 是否預(yù)警：否; TOP期刊：是

出版信息：: 出版地區(qū)：United States; 出版商：Public Library of Science; 出版語言：English; 出版周期：Monthly; 出版年份：2005; 研究方向：Environmental Science-Ecology

評價(jià)信息：: 影響因子：3.8; H-index：138; CiteScore指數(shù)：7.1; SJR指數(shù)：1.652; SNIP指數(shù)：1.085

發(fā)文數(shù)據(jù)：: Gold OA文章占比：99.67%; 研究類文章占比：98.90%; 年發(fā)文量：637; 自引率：0.0465...; 開源占比：0.9896; 出版撤稿占比：0; 出版國人文章占比：0.04; OA被引用占比：1

投稿咨詢加急見刊

英文簡介期刊介紹 CiteScore數(shù)據(jù) 中科院SCI分區(qū) JCR分區(qū) 發(fā)文數(shù)據(jù) 常見問題

英文簡介Plos Computational Biology期刊介紹

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.

期刊簡介Plos Computational Biology期刊介紹

《Plos Computational Biology》自2005出版以來，是一本生物學(xué)優(yōu)秀雜志。致力于發(fā)表原創(chuàng)科學(xué)研究結(jié)果，并為生物學(xué)各個領(lǐng)域的原創(chuàng)研究提供一個展示平臺，以促進(jìn)生物學(xué)領(lǐng)域的的進(jìn)步。該刊鼓勵先進(jìn)的、清晰的闡述，從廣泛的視角提供當(dāng)前感興趣的研究主題的新見解，或?qū)彶槎嗄陙砟硞€重要領(lǐng)域的所有重要發(fā)展。該期刊特色在于及時報(bào)道生物學(xué)領(lǐng)域的最新進(jìn)展和新發(fā)現(xiàn)新突破等。該刊近一年未被列入預(yù)警期刊名單，目前已被權(quán)威數(shù)據(jù)庫SCIE收錄，得到了廣泛的認(rèn)可。

該期刊投稿重要關(guān)注點(diǎn)：

預(yù)計(jì)審稿時間： 32 Weeks
國際TOP期刊
生物學(xué)
BIOCHEMICAL RESEARCH METHODS
SCIE
中科院2區(qū)
非預(yù)警

Cite Score數(shù)據(jù)（2024年最新版）Plos Computational Biology Cite Score數(shù)據(jù)

CiteScore：7.1
SJR：1.652
SNIP：1.085

學(xué)科類別	分區(qū)	排名	百分位
大類：Mathematics 小類：Modeling and Simulation	Q1	32 / 324	90%
大類：Mathematics 小類：Ecology, Evolution, Behavior and Systematics	Q1	87 / 721	88%
大類：Mathematics 小類：Computational Theory and Mathematics	Q1	23 / 176	87%
大類：Mathematics 小類：Ecology	Q1	63 / 461	86%
大類：Mathematics 小類：Genetics	Q2	97 / 347	72%
大類：Mathematics 小類：Cellular and Molecular Neuroscience	Q2	34 / 97	65%
大類：Mathematics 小類：Molecular Biology	Q2	163 / 410	60%

CiteScore 是由Elsevier(愛思唯爾)推出的另一種評價(jià)期刊影響力的文獻(xiàn)計(jì)量指標(biāo)。反映出一家期刊近期發(fā)表論文的年篇均引用次數(shù)。CiteScore以Scopus數(shù)據(jù)庫中收集的引文為基礎(chǔ)，針對的是前四年發(fā)表的論文的引文。CiteScore的意義在于，它可以為學(xué)術(shù)界提供一種新的、更全面、更客觀地評價(jià)期刊影響力的方法，而不僅僅是通過影響因子(IF)這一單一指標(biāo)來評價(jià)。

歷年Cite Score趨勢圖

中科院SCI分區(qū)Plos Computational Biology 中科院分區(qū)

中科院 2023年12月升級版 綜述期刊：否 Top期刊：否

大類學(xué)科	分區(qū)	小類學(xué)科	分區(qū)
生物學(xué)	2區(qū)	BIOCHEMICAL RESEARCH METHODS 生化研究方法 MATHEMATICAL & COMPUTATIONAL BIOLOGY 數(shù)學(xué)與計(jì)算生物學(xué)	2區(qū) 2區(qū)

中科院分區(qū)表 是以客觀數(shù)據(jù)為基礎(chǔ)，運(yùn)用科學(xué)計(jì)量學(xué)方法對國際、國內(nèi)學(xué)術(shù)期刊依據(jù)影響力進(jìn)行等級劃分的期刊評價(jià)標(biāo)準(zhǔn)。它為我國科研、教育機(jī)構(gòu)的管理人員、科研工作者提供了一份評價(jià)國際學(xué)術(shù)期刊影響力的參考數(shù)據(jù)，得到了全國各地高校、科研機(jī)構(gòu)的廣泛認(rèn)可。

中科院分區(qū)表 將所有期刊按照一定指標(biāo)劃分為1區(qū)、2區(qū)、3區(qū)、4區(qū)四個層次，類似于“優(yōu)、良、及格”等。最開始，這個分區(qū)只是為了方便圖書管理及圖書情報(bào)領(lǐng)域的研究和期刊評估。之后中科院分區(qū)逐步發(fā)展成為了一種評價(jià)學(xué)術(shù)期刊質(zhì)量的重要工具。

歷年中科院分區(qū)趨勢圖

JCR分區(qū)Plos Computational Biology JCR分區(qū)

2023-2024 年最新版

按JIF指標(biāo)學(xué)科分區(qū)	收錄子集	分區(qū)	排名	百分位
學(xué)科：BIOCHEMICAL RESEARCH METHODS	SCIE	Q1	15 / 85	82.9%
學(xué)科：MATHEMATICAL & COMPUTATIONAL BIOLOGY	SCIE	Q1	11 / 65	83.8%

按JCI指標(biāo)學(xué)科分區(qū)	收錄子集	分區(qū)	排名	百分位
學(xué)科：BIOCHEMICAL RESEARCH METHODS	SCIE	Q1	15 / 85	82.94%
學(xué)科：MATHEMATICAL & COMPUTATIONAL BIOLOGY	SCIE	Q1	12 / 65	82.31%

JCR分區(qū)的優(yōu)勢在于它可以幫助讀者對學(xué)術(shù)文獻(xiàn)質(zhì)量進(jìn)行評估。不同學(xué)科的文章引用量可能存在較大的差異，此時單獨(dú)依靠影響因子(IF)評價(jià)期刊的質(zhì)量可能是存在一定問題的。因此，JCR將期刊按照學(xué)科門類和影響因子分為不同的分區(qū)，這樣讀者可以根據(jù)自己的研究領(lǐng)域和需求選擇合適的期刊。

歷年影響因子趨勢圖

發(fā)文數(shù)據(jù)

2023-2024 年國家/地區(qū)發(fā)文量統(tǒng)計(jì)

國家/地區(qū)數(shù)量
USA1072
England323
GERMANY (FED REP GER)284
France170
CHINA MAINLAND125
Canada123
Switzerland113
Spain99
Netherlands91
Australia85

本刊中國學(xué)者近年發(fā)表論文

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