scholarly journals Effect of training datasets on support vector machine prediction of protein-protein interactions

PROTEOMICS ◽  
2005 ◽  
Vol 5 (4) ◽  
pp. 876-884 ◽  
Author(s):  
Siaw Ling Lo ◽  
Cong Zhong Cai ◽  
Yu Zong Chen ◽  
Maxey C. M. Chung
Keyword(s):  
Support Vector Machine ◽  
Protein Interactions ◽  
Support Vector ◽  
Protein Protein Interactions

scholarly journals Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

2012 ◽  
Vol 2012 ◽  
pp. 1-23
Author(s):  
J. M. Urquiza ◽  
I. Rojas ◽  
H. Pomares ◽  
J. Herrera ◽  
J. P. Florido ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Protein Interactions ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Machine Model ◽  
Cellular Processes ◽  
Parallel Feature ◽  
Ppi Prediction

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.

scholarly journals 2P2I HUNTER : a tool for filtering orthosteric protein–protein interaction modulators via a dedicated support vector machine

2014 ◽  
Vol 11 (90) ◽  
pp. 20130860 ◽  
Author(s):  
Véronique Hamon ◽  
Raphael Bourgeas ◽  
Pierre Ducrot ◽  
Isabelle Theret ◽  
Laura Xuereb ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Chemical Space ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Target Class ◽  
Chemical Library ◽  
Protein Protein Interaction ◽  
Svm Model

Over the last 10 years, protein–protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this ‘high-hanging fruit’ is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database ( http://2p2idb.cnrs-mrs.fr/ ) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard D ragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns . This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions

2019 ◽  
Vol 40 (11) ◽  
pp. 1233-1242 ◽  
Author(s):  
Sandra Romero-Molina ◽  
Yasser B. Ruiz-Blanco ◽  
Mirja Harms ◽  
Jan Münch ◽  
Elsa Sanchez-Garcia
Keyword(s):  
Support Vector Machine ◽  
Protein Interactions ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Machine Model
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