Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology
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 hi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/897289 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552583795834880 |
|---|---|
| author | J. M. Urquiza I. Rojas H. Pomares J. Herrera J. P. Florido O. Valenzuela |
| author_facet | J. M. Urquiza I. Rojas H. Pomares J. Herrera J. P. Florido O. Valenzuela |
| author_sort | J. M. Urquiza |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a195f68307cb486da82e41cd3b8f41ce |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-a195f68307cb486da82e41cd3b8f41ce2025-02-03T05:58:19ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/897289897289Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering MethodologyJ. M. Urquiza0I. Rojas1H. Pomares2J. Herrera3J. P. Florido4O. Valenzuela5Department of Computer Architecture and Computer Technology, University of Granada, 18017 Granada, SpainDepartment of Computer Architecture and Computer Technology, University of Granada, 18017 Granada, SpainDepartment of Computer Architecture and Computer Technology, University of Granada, 18017 Granada, SpainDepartment of Computer Architecture and Computer Technology, University of Granada, 18017 Granada, SpainDepartment of Computer Architecture and Computer Technology, University of Granada, 18017 Granada, SpainDepartment of Applied Mathematics, University of Granada, 18017 Granada, SpainProtein-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.http://dx.doi.org/10.1155/2012/897289 |
| spellingShingle | J. M. Urquiza I. Rojas H. Pomares J. Herrera J. P. Florido O. Valenzuela Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology Journal of Applied Mathematics |
| title | Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology |
| title_full | Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology |
| title_fullStr | Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology |
| title_full_unstemmed | Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology |
| title_short | Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology |
| title_sort | selecting negative samples for ppi prediction using hierarchical clustering methodology |
| url | http://dx.doi.org/10.1155/2012/897289 |
| work_keys_str_mv | AT jmurquiza selectingnegativesamplesforppipredictionusinghierarchicalclusteringmethodology AT irojas selectingnegativesamplesforppipredictionusinghierarchicalclusteringmethodology AT hpomares selectingnegativesamplesforppipredictionusinghierarchicalclusteringmethodology AT jherrera selectingnegativesamplesforppipredictionusinghierarchicalclusteringmethodology AT jpflorido selectingnegativesamplesforppipredictionusinghierarchicalclusteringmethodology AT ovalenzuela selectingnegativesamplesforppipredictionusinghierarchicalclusteringmethodology |