pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning  

作　　者：Yutao Shao Kuo-Chen Chou Yutao Shao;Kuo-Chen Chou(Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China;Gordon Life Science Institute, Boston, MA, USA)

机构地区：[1]Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China [2]Gordon Life Science Institute, Boston, MA, USA

出　　处：《Natural Science》2020年第6期388-399,共12页自然科学期刊（英文）

摘　　要：<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p><p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>

关 键 词：CORONAVIRUS Virus Proteins Multi-Label System Deep Learning Five-Steps Rule PseAAC 

分 类 号：O17[理学—数学]