engleski

Classification of 1D-Signal Types Using Semi- Supervised Deep Learning

In this thesis, we proposed methods for detecting the type of the coverage graph based on semi-supervised deep learning models: autoencoders and generative adversarial networks. We evaluated the performance of each model based on the dataset that contained reads from multiple reference genomes. We have manually labeled some of the data and compared the results of all models with respect to the number of labeled examples that were used during the training process. We have embedded this detection in the assembly process and achieved good results. The source code is available at https://github.com/tomislavsebrek/diplomski.

deep learning, autoencoder, generative adversarial network, semi-supervised learning, coverage graph, chimeric read, repeat read

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