Naslov
Identification of 1D-Signal Types Using Unsupervised Deep Learning

Autori
Tomljanović, Jan

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

Fakultet
Fakultet Elektrotehnike i Računarstva

Mjesto
Zagreb

Datum
10.07

Godina
2017

Stranica
65

Mentor
Šikić, Mile

Ključne riječi
bioinformatics, unsupervised learning, deep learning, autoencoders

Sažetak
During de novo genome assembly process, certain types of sequenced reads can cause problems during genome reconstruction. Goal of this thesis is to learn more about possible types of reads and classification of those reads using unsupervised learning. Coverage graphs of reads are generated using read overlaps and those coverage graphs are further analysed. Autoencoder is used to compress the signal, i.e. the coverage graph, and clustering algorithm is then applied to the compressed data. Variational and denoising autoencoders along with k-means and spectral clustering algorithms are used. Visualisation of found clusters is performed along with semantic analysis. Signal classification quality using unsupervised learning is estimated.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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