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

Autori
Šebrek, Tomislav

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet Elektrotehnike i Računarstva

Mjesto
Zagreb

Datum
10.07

Godina
2017

Stranica
60

Mentor
Šikić, Mile

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA