New approach based on Generative Adversarial Network (GAN) and Support Vector Machine (SVM) to identify in-silico candidates for reference genes.
The proposed method is divided into two main steps. First, the GAN is used to increase a small number of reference genes found in the public RNA-seq dataset of Escherichia coli. Second, a one-class SVM based on novelty detection is evaluated using some real reference genes and synthetic ones generated by the GAN architecture in the first step.
The results show that increasing the dataset using the proposed GAN architecture improves the classifier score by 19%, making the proposed method have a recall score of 85% on the test data.
- Change one class SVM parameters to achieve better results.
- Add Linear classifiers (Perceptron, Least square, Gradient descent, Widrow Hoff,etc).
- Add Parzen (Kernel density estimation), Gaussian mixture model for classification.
- Using Dimension reduction methods (Autoencoder, PCA) and classification of data with new dimensions.
- Add supervised mode (MLP and RBF network) with generate outlier data.
You can see paper at: https://link.springer.com/chapter/10.1007/978-3-030-58799-4_51