selecting negative samples for ppi prediction using hierarchical clustering methodology
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ID: 158929
2012
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Abstract
Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected
suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.
| Reference Key |
urquiza2012journalselecting
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|---|---|
| Authors | ;J. M. Urquiza;I. Rojas;H. Pomares;J. Herrera;J. P. Florido;O. Valenzuela |
| Journal | Chemico-biological interactions |
| Year | 2012 |
| DOI |
10.1155/2012/897289
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| URL | |
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