Optimized bio-inspired kernels with twin support vector machine using low identity sequences to solve imbalance multiclass classification
The function of enzymes is performed differently depending on their bio-chemical mechanisms and important to the prediction of protein structure and function. In order to overcome the weaknesses of imbalance data distribution in subclasses prediction we proposed Bio-Twin Support Vector Machine (Bio–...
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| Main Authors: | , , , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
Triveni Enterprises, Lucknow (India)
2019
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| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/4626/1/AJ%202019%20%28299%29.pdf http://eprints.uthm.edu.my/4626/ http://doi.org/10.22438/jeb/40/3(SI)/Sp-21 |
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| 總結: | The function of enzymes is performed differently depending on their bio-chemical mechanisms and important to the prediction of protein structure and function. In order to overcome the weaknesses of imbalance data distribution in subclasses prediction we proposed Bio-Twin Support Vector Machine (Bio–TWSVM). The TWSVM approach as also allow for kernel optimization where in this study we have introduced the bio-inspired kernels such as the Fisher, spectrum and mismatch kernels which at the same time incorporate the biological information regarding the protein evolution in the classification process. |
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