Predicting protein subchloroplast locations:the 10th anniversary

作     者：Jian SUN Pu-Feng DU Jian SUN;Pu-Feng DU

作者机构：College of Intelligence and ComputingTianjin UniversityTianjin300350China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2021年第15卷第2期

页      面：1-11页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 081203[工学-计算机应用技术] 08[工学] 071009[理学-细胞生物学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by National Key R&D Program of China(2018YFC0910405),The National Natural Science Foundation of China(NSFC,Grant No.61872268) Open Project Funding of CAS Key Lab of Network Data Science and Technology,Institute of Computing Technology,Chinese Academy of Sciences(CASNDST201705) 

主　　题：subchloroplast locations sequence features performance measures online services machine learning 

摘      要：Chloroplast is a type of subcellular organelle in green plants and *** is the main subcellular organelle for conducting photosynthetic *** proteins,which localize within the chloroplast,are responsible for the photosynthetic process at molecular *** chloroplast can be further divided into several *** in different compartments are related to different steps in the photosynthetic *** the molecular function of a protein is highly correlated to the exact cellular localization,pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic *** process for determining protein subchloroplast location is always costly and time ***,computational approaches were developed to predict the protein subchloroplast locations from the primary *** the last decades,more than a dozen studies have tried to predict protein subchloroplast locations with machine learning *** sequence features and various machine learning algorithms have been introduced in this research *** this review,we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast *** compare these studies in the aspects of benchmarking datasets,sequence features,machine learning algorithms,predictive performances,and the implementation *** summarized the progress and current status in this special research *** also try to figure out the most possible future works in predicting protein subchloroplast *** hope this review not only list all existing works,but also serve the readers as a useful resource for quickly grasping the big picture of this research *** also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.