Prediction of Outer Membrane Proteins by Support Vector Machines Using Combinations of Gapped Amino Acid Pair Compositions

2006 ◽  
Author(s):  
Ssu-Hua Huang ◽  
Ru-Sheng Liu ◽  
Chien-Yu Chen ◽  
Ya-Ting Chao ◽  
Shu-Yuan Chen
Keyword(s):  
Amino Acid ◽  
Support Vector Machines ◽  
Membrane Proteins ◽  
Outer Membrane ◽  
Outer Membrane Proteins ◽  
Support Vector ◽  
Amino Acid Pair ◽  
Vector Machines

scholarly journals Discrimination of outer membrane proteins using support vector machines

2005 ◽  
Vol 21 (23) ◽  
pp. 4223-4229 ◽  
Author(s):  
K.-J. Park ◽  
M. M. Gromiha ◽  
P. Horton ◽  
M. Suwa
Keyword(s):  
Support Vector Machines ◽  
Membrane Proteins ◽  
Outer Membrane ◽  
Outer Membrane Proteins ◽  
Support Vector ◽  
Vector Machines

scholarly journals Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Seizi Someya ◽  
Masanori Kakuta ◽  
Mizuki Morita ◽  
Kazuya Sumikoshi ◽  
Wei Cao ◽  
...  
Keyword(s):  
Amino Acid ◽  
Support Vector Machines ◽  
Binding Proteins ◽  
Prediction Method ◽  
Amino Acid Sequences ◽  
Support Vector ◽  
Carbohydrate Binding ◽  
Genome Database ◽  
Vector Machines ◽  
Carbohydrate Binding Proteins

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

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