雜志介紹

Plos Computational Biology雜志介紹

《Plos Computational Biology》是一本以English為主的開(kāi)放獲取國(guó)際優(yōu)秀期刊，中文名稱(chēng)Plos 計(jì)算生物學(xué)，本刊主要出版、報(bào)道生物學(xué)-BIOCHEMICAL RESEARCH METHODS領(lǐng)域的研究動(dòng)態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗(yàn)和科研成果，介紹該領(lǐng)域有關(guān)本專(zhuān)業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國(guó)際權(quán)威數(shù)據(jù)庫(kù)SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動(dòng)作用，也得到了本專(zhuān)業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為3.8，最新CiteScore 指數(shù)為7.1。

本刊近期中國(guó)學(xué)者發(fā)表的論文主要有：

• 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

• A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions

  Author: Ma, Mei; Lei, Xiujuan

• HiSV: A control-free method for structural variation detection from Hi-C data

  Author: Li, Junping; Gao, Lin; Ye, Yusen

• 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

英文介紹

Plos Computational Biology雜志英文介紹

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.