File Entry: One-Class Classifier to Predict Protein-Protein Interactions based on Hydrophobicity Properties

Created: 2012-05-11 02:25:30
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Version created on: 2012-05-11 02:25:30

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 Protein-protein interactions are important in a

wide range of biological processes. The development of drugs
that target such interactions is a very active research field. Hence
predicting protein-protein interactions represent an important
challenge in bioinformatics research. Machine learning
techniques have been applied to predict protein-protein
interactions. Most of these techniques address this problem as a
binary classification problem. While it is easy to get a dataset of
interacting protein as positive example, there are no
experimentally confirmed noninteracting proteins to be
considered as a negative set.
Therefore, in this paper we solve this problem as a one-class
classification problem using One-Class SVM (OCSVM). The
hydrophobicity properties have been used in this research as the
protein sequence feature.
Using only positive examples (interacting protein pairs) for
training, the OCSVM achieves accuracy of 72% using RBF
kernel. These results imply that protein-protein interaction can
be predicted using oneclass classifier with reliable accuracy.

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