Large‐scale plant protein subcellular location prediction

AbstractCurrent plant genome sequencing projects have called for development of novel and powerful high throughput tools for timely annotating the subcellular location of uncharacterized plant proteins. In view of this, an ensemble classifier, Plant‐PLoc, formed by fusing many basic individual classifiers, has been developed for large‐scale subcellular location prediction for plant proteins. Each of the basic classifiers was engineered by the K‐Nearest Neighbor (KNN) rule. Plant‐PLoc discriminates plant proteins among the following 11 subcellular locations: (1) cell wall, (2) chloroplast, (3) cytoplasm, (4) endoplasmic reticulum, (5) extracell, (6) mitochondrion, (7) nucleus, (8) peroxisome, (9) plasma membrane, (10) plastid, and (11) vacuole. As a demonstration, predictions were performed on a stringent benchmark dataset in which none of the proteins included has ≥25% sequence identity to any other in a same subcellular location to avoid the homology bias. The overall success rate thus obtained was 32–51% higher than the rates obtained by the previous methods on the same benchmark dataset. The essence of Plant‐PLoc in enhancing the prediction quality and its significance in biolog