Adaptive neural network-based clustering of yeast protein: protein interactions

Authors:
Jae-Hong Eom;Byoung-Tak Zhang
Affiliations:
Biointelligence Lab., School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Lab., School of Computer Science and Engineering, Seoul National University, Seoul, South Korea
Venue:
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Year:
2004

Citing 5
Cited 3

A massively parallel architecture for a self-organizing neural pattern recognition machine

Computer Vision, Graphics, and Image Processing
C4.5: programs for machine learning

C4.5: programs for machine learning
Gene functional classification from heterogeneous data

RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Adaptive Neural Network Clustering of Web Users

Computer
Probabilistic inference of molecular networks from noisy data sources

Bioinformatics

Prediction of yeast protein-protein interactions by neural feature association rule

ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Prediction of protein interaction with neural network-based feature association rule mining

ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Neural feature association rule mining for protein interaction prediction

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we presents an adaptive neural network based clustering method to group protein–protein interaction data according to their functional categories for new protein interaction prediction in conjunction with information theory based feature selection. Our technique for grouping protein interaction is based on ART-1 neural network. The cluster prototype constructed with existing protein interaction data is used to predict the class of new protein interactions. The protein interaction data of S.cerevisiae (bakers yeast) from MIPS and SGD are used. The clustering performance was compared with traditional k-means clustering method in terms of cluster distance. According to the experimental results, the proposed method shows about 89.7% clustering accuracy and the feature selection filter boosted overall performances about 14.8%. Also, inter-cluster distances of cluster constructed with ART-1 based clustering method have shown high cluster quality.