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Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology
- Source :
- J. Appl. Math., Journal of Applied Mathematics, Vol 2012 (2012)
- Publication Year :
- 2012
- Publisher :
- Hindawi Limited, 2012.
-
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.
- Subjects :
- Training set
Article Subject
Computer science
business.industry
lcsh:Mathematics
Applied Mathematics
Feature selection
Mutual information
Construct (python library)
lcsh:QA1-939
Machine learning
computer.software_genre
Hierarchical clustering
Set (abstract data type)
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Relevance (information retrieval)
Artificial intelligence
Data mining
business
computer
Subjects
Details
- ISSN :
- 16870042 and 1110757X
- Volume :
- 2012
- Database :
- OpenAIRE
- Journal :
- Journal of Applied Mathematics
- Accession number :
- edsair.doi.dedup.....24ea91866c88f2536438268003299989