Parallel prediction of protein-protein interactions using proximal SVM

Authors:
Yoojin Chung;Sang-Young Cho;Sung Y. Shin
Affiliations:
Computer Science & Information Communications Engineering Division, Hankuk University of Foreign Studies, Yongin, Kyonggi-do, Korea;Computer Science & Information Communications Engineering Division, Hankuk University of Foreign Studies, Yongin, Kyonggi-do, Korea;EE and Computer Science Department, South Dakota State University, Brookings, SD
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

Citing 4
Cited 0

Neural networks for pattern recognition

Neural networks for pattern recognition
Making large-scale support vector machine learning practical

Advances in kernel methods
Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
Predicting protein-protein interactions from one feature using SVM

IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence

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Abstract

In general, the interactions between proteins are fundamental to a broad area of biological functions. In this paper, we try to predict protein-protein interactions in parallel on a 12-node PC-cluster using domains of a protein. For this, we use a hydrophobicity among protein's amino acid's physicochemical feature and a support vector machine (SVM) among machine learning techniques. According to the experiments, we get approximately 60% average accuracy with 5 trials and we obtained an average speed-up of 5.11 with a 12-node cluster using a proximal SVM.