StruLocPred: structure-based protein subcellular localisation prediction using multi-class support vector machine

Authors:
Wengang Zhou;Julie A. Dickerson
Affiliations:
Bioinformatics and Computational Biology Program, Electrical and Computer Engineering Department, Virtual Reality Applications Center, Iowa State University, Ames, IA 50011, USA;Bioinformatics and Computational Biology Program, Electrical and Computer Engineering Department, Virtual Reality Applications Center, Iowa State University, Ames, IA 50011, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2012

Citing 6
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Prediction of protein subcellular locations using fuzzy k-NN method

Bioinformatics
BaCelLo

Bioinformatics
MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition

Bioinformatics
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

A novel class dependent feature selection method for cancer biomarker discovery

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.