title: PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction.
abstract: Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ~10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/.
authors.affiliation: College of Life Sciences, Zhejiang University, Hangzhou, China.
authors.forename: Lili
authors.initials: L
authors.lastname: Liu
authors.forename: Zijun
authors.initials: Z
authors.lastname: Zhang
authors.forename: Qian
authors.initials: Q
authors.lastname: Mei
authors.forename: Ming
authors.initials: M
authors.lastname: Chen
mesh_terms: D020407:Internet; D042541:Intracellular Space / Q000378:metabolism*; D010940:Plant Proteins / Q000378:metabolism*; D010944:Plants / Q000378:metabolism*; D012984:Software
chemical_list: D010940:Plant Proteins