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Construction of precise support vector machine based models for predicting promoter strength |
Hailin Meng1,Yingfei Ma2,Guoqin Mai2,Yong Wang3( ),Chenli Liu1,2( ) |
1. Bioengineering Research Center, Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511458, China
2. Center for Synthetic Biology Engineering Research, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3. Chinese Academy of Sciences Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China |
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Abstract Background: The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network (ANN) has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine (SVM) is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem.
Methods: To confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set (≥10 sequences) for model training and a test set (≥10 sequences) for model test.
Results: The results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a high-performance model was finally trained, with a high squared correlation coefficient for fitting the training set (R2>0.99) and the test set (R2>0.98), both of which are better than that of ANN obtained by our previous work.
Conclusions: Our results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.
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Author Summary Machine learning models can learn knowledge from a given dataset and make predictions on unknown data. These technologies are widely used in artificial intelligence and made tremendous progress, triggering the coming era of “Industry 4.0”. In life sciences, introduction of such methods has greatly promoted the development of the discipline, especially modeling in bioinformatics and systems biology. As a powerful machine learning method suitable for small sample learning, support vector machine (SVM) was introduced into the field of promoter strength prediction. The good performance of SVM models demonstrates a promising application prospect of this method in prediction of promoter strength. |
Keywords
support vector machine model
quantitative prediction
promoter strength
machine learning
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Fund: |
Corresponding Author(s):
Yong Wang,Chenli Liu
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Online First Date: 15 February 2017
Issue Date: 22 March 2017
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