Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines

2009 ◽  
Author(s):  
Guan Ning Lin ◽  
Zheng Wang ◽  
Dong Xu ◽  
Jianlin Cheng
Keyword(s):  
Protein Folding ◽  
Support Vector Machines ◽  
Secondary Structures ◽  
Support Vector ◽  
Folding Rates ◽  
Vector Machines

Protein Folding Classification Through Multicategory Discrete SVM

2011 ◽  
pp. 116-130
Author(s):  
Carlotta Orsenigo ◽  
Carlo Vercellis
Keyword(s):  
Protein Folding ◽  
Support Vector Machines ◽  
Prediction Accuracy ◽  
Classification Problem ◽  
New Drugs ◽  
Support Vector ◽  
Multicategory Classification ◽  
Vector Machines ◽  
Benchmark Datasets ◽  
Folding Structure

In the context of biolife science, predicting the folding structure of a protein plays an important role for investigating its function and discovering new drugs. Protein folding recognition can be naturally cast in the form of a multicategory classification problem, that appears challenging due to the high number of folds classes. Thus, in the last decade several supervised learning methods have been applied in order to discriminate between proteins characterized by different folds. Recently, discrete support vector machines have been introduced as an effective alternative to traditional support vector machines. Discrete SVM have shown to outperform other competing classification techniques both on binary and multicategory benchmark datasets. In this paper, we adopt discrete SVM for protein folding classification. Computational tests performed on benchmark datasets empirically support the effectiveness of discrete SVM, which are able to achieve the highest prediction accuracy.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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