Semi-supervised protein function prediction via sequential linear neighborhood propagation

Authors:
Jingyan Wang;Yongping Li;Ying Zhang;Jianhua He
Affiliations:
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading District, Shanghai, P.R. China;Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading District, Shanghai, P.R. China;Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading District, Shanghai, P.R. China;Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Jiading District, Shanghai, P.R. China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

Citing 6
Cited 1

Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps

Bioinformatics
Exploiting indirect neighbours and topological weight to predict protein function from protein--protein interactions

Bioinformatics
Graph-Based Semisupervised Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Neighborhood Propagation and Its Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graph-Based Semi-supervised Algorithm for Protein Function Prediction from Interaction Maps

Learning and Intelligent Optimization
Integrated protein interaction networks for 11 microbes

RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology

Self-adaptive local Fisher discriminant analysis for semi-supervised image recognition

International Journal of Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Predicting protein function is one of the most challenging problems of the post-genomic era. The development of experimental methods for genome scale analysis of molecular interaction networks has provided new approaches to inferring protein function. In this paper we introduce a new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP), which addresses the problem of the classification of partially labeled protein interaction networks. The proposed SLNP firstly constructs a sequence of node sets according to their shortest distance to the labeled nodes, and then predicts the function of the unlabel proteins from the set closer to labeled one, using Linear Neighborhood Propagation. Its performance is assessed on the Saccharomyces cerevisiae PPI network data sets with good results compared with three current state-of-the-art algorithms.