Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology
Keyword(s):
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.
2019 ◽
Vol 40
(11)
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pp. 1233-1242
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Keyword(s):
2021 ◽
Vol 59
(4)
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pp. 825-839
Keyword(s):
2010 ◽
Vol 3
(6)
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pp. 797-804
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Keyword(s):
Keyword(s):