Sequence-based protein-protein interaction prediction via support vector machine

2010 ◽  
Vol 23 (5) ◽  
pp. 1012-1023 ◽  
Author(s):  
Yongcui Wang ◽  
Jiguang Wang ◽  
Zhixia Yang ◽  
Naiyang Deng
Keyword(s):  
Support Vector Machine ◽  
Protein Interaction ◽  
Support Vector ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction

scholarly journals Predicting Protein-protein Interaction Using Amino Acid Sequence Information: A Computational Approach

2010 ◽  
Vol 20 (1) ◽  
pp. 37-45
Author(s):  
Mohammad Shoyaib ◽  
M. Abdullah-Al-Wadud ◽  
Syed Murtuza Baker ◽  
Mohammad Nurul Islam ◽  
Oksam Chae
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Protein Interaction ◽  
Computational Approach ◽  
Support Vector ◽  
Sequence Information ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Protein Interaction Prediction

An improved computational approach which implements a protein-protein interaction prediction system based on the sequence information of a protein has been presented. A Support Vector Machine (SVM) is trained with this sequence information to predict the interactions. This interaction prediction technique exhibits 79.81% accuracy over a wide range of data, which is a significant improvement over other conventional computational protein-protein interaction prediction methods. Key words: Protein-protein interaction, Amino acid sequence, Computational approach D.O.I. 10.3329/ptcb.v20i1.5963 Plant Tissue Cult. & Biotech. 20(1): 37-45, 2010 (June)  

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