Journal Title：Plos Computational Biology

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.

PLOS Computational Biology 刊登了具有特殊意義的論文，這些論文通過應用計算方法，進一步加深了我們對各個尺度的生命系統(tǒng)的理解——從分子和細胞到患者群體和生態(tài)系統(tǒng)。讀者包括生命科學家和計算科學家，他們可以將這里提出的重要發(fā)現(xiàn)提升到新的發(fā)現(xiàn)水平。

研究文章必須聲明為屬于相關部分。有關各部分的更多信息，請參閱提交指南。

研究文章應該模擬生物系統(tǒng)的各個方面，展示方法和科學的新穎性，并提供深刻的新生物學見解。

通常，通過計算進行的生物學發(fā)現(xiàn)的可靠性和重要性應該通過實驗研究進行驗證和豐富。發(fā)表時不需要包含實驗驗證，但應盡可能引用。通過計算對一個適度的生物學發(fā)現(xiàn)進行實驗驗證并不能使手稿適合 PLOS Computational Biology。

專門指定為方法論文的研究文章應描述已證明具有特殊重要性的杰出方法，或有望提供新的生物學見解。該方法必須已被廣泛采用，或有望被廣大用戶廣泛采用。只有當這些增強功能帶來卓越的新功能時，才會考慮對現(xiàn)有已發(fā)布方法的增強。

Plos Computational Biology創(chuàng)刊于2005年，由Public Library of Science出版商出版，收稿方向涵蓋Environmental Science - Ecology全領域，此刊是該細分領域中屬于非常不錯的SCI期刊，在行業(yè)細分領域中學術影響力較大，專業(yè)度認可很高，所以對原創(chuàng)文章要求創(chuàng)新性較高，如果您的文章質(zhì)量很高，可以嘗試。平均審稿速度 32 Weeks ，影響因子指數(shù)3.8，該期刊近期沒有被列入國際期刊預警名單，廣大學者值得一試。