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A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers

Authors: Yanfen Lyu; Xinqi Gong;

A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers

Abstract

Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein–protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

Related Organizations
Keywords

Support Vector Machine, a two-layer SVM ensemble-classifier, feature combination engineering, Organic chemistry, Computational Biology, Proteins, Article, QD241-441, trimer protein–protein complexes, Protein Multimerization, Protein Structure, Quaternary

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    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Average
Green
gold