Background:
Reactive oxygen species (ROS) has many roles in the body such as cell signaling, homeostasis or protection from harmful bacteria. However, too much ROS in the body will damage lipids, proteins, and DNA. Many studies show that many environmental factors increase the amount of ROS produced in the body. Antioxidant proteins are responsible for neutralizing these ROS or free radicals. Although the amount of data on protein sequences has increased over the last two decades, we still lack bioinformatics tools to be able to accurately identify antioxidant protein sequences while biochemical methods to determine antioxidant proteins are very expensive and time consuming, so a machine learning approach must be used to speed up the computation. In this study.
Methods:
we propose a new method that combines convolutional neural network and Random Forest using two features, the normalized PSSM and the best selected feature of the ProtBert output.
Result:
Our model gave very good results on the independent test dataset with 97.3% sensitivity and 95.9% specificity. Comparison with current state of the art models shows that our model is superior.
Conclusion:
We have also installed iAnt as an online web site with a friendly interface available at http://antixiodant.nguyenhongquang.edu.vn. iAnt has been developed to accurately identify the antioxidant protein. It shows results outperforming the existing state-of-the-art methods, and it is available online.
This paper will present a new method of identifying Vietnamese voice commands using Google speech recognition (GSR) service results. The problem is that the percentage of correct identifications of Vietnamese voice commands in the Google system is not high. We propose a supervised machine-learning approach to address cases in which Google incorrectly identifies voice commands. First, we build a voice command dataset that includes hypotheses of GSR for each corresponding voice command. Next, we propose a correction system using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that the correction system reduces errors in recognizing Vietnamese voice commands from 35.06% to 7.08% using the SVM model and 5.15% using the CNN model.
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