Naslov
Classification of 1D-Signal Types Using Deep Learning

Autori
Floreani, Filip

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

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
04.07

Godina
2019

Stranica
50

Mentor
Šikić, Mile

Ključne riječi
bioinformatics, sequence assembly, false overlaps, deep learning

Sažetak
The de novo genome assembly process is based on overlapping and analyzing short reads of genetic information. Due to various technical challenges, certain types of false overlaps can also be generated, which greatly impedes successful reconstruction. One of the methods for detecting such overlaps is by generating a 1D-signal for each read, which can then be used to determine its exact overlap type. This thesis proposes several deep learning methods for classifying these signals, including 1D-convolutional and recurrent networks, as well as autoencoders. A detailed comparison of their application on real-world data is also included.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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