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Large scale prediction of protein interactions by a SVM-based method

Marangoni, F., Barberis, Matteo and Botta, M. (2003) Large scale prediction of protein interactions by a SVM-based method In: Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, 04-07 Jun 2003, Vietri sul Mare, Italy.

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Abstract

A number of techniques have been developed in order to address issues such as genome, trascriptome and proteome analysis. However, a time and cost effective technique for interactome analysis is still lacking. Lots of methods for the predicion of protein - protein interacions have been developed: some of them are based on high quality alignment of sequences, others are based on the tridimensional features of proteins, but they all bear strong limitations that make impossible their large scale application. Recently, an SVM-based machine learning approach has been used to address this topic. Although the method was able to correctly classify 80% of the test samples, it was not applied to the prediction of yet unknown interactions. In this work, we address this topic and show that an optimized, SVM-based machine learning approach trained with combinations of shuffled sequences as examples of lack of interaction is unable to make large scale predictions of interaction. © Springer-Verlag Berlin Heidelberg 2003.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine
Authors :
NameEmailORCID
Marangoni, F.
Barberis, Matteom.barberis@surrey.ac.uk
Botta, M.
Date : 29 September 2003
DOI : 10.1007/978-3-540-45216-4_33
Copyright Disclaimer : © Springer-Verlag Berlin Heidelberg 2003
Uncontrolled Keywords : Saccharomyces Cerevisiae; Standardization Length; Cost Effective Technique; Saccharomyces Genome Database; Protein Interactions
Related URLs :
Depositing User : Clive Harris
Date Deposited : 16 Apr 2019 13:21
Last Modified : 16 Apr 2019 13:21
URI: http://epubs.surrey.ac.uk/id/eprint/851030

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