Identification of 1D-Signal Types Using Unsupervised Deep Learning (CROSBI ID 411202)
Ocjenski rad | diplomski rad
Podaci o odgovornosti
Tomljanović, Jan
Šikić, Mile
engleski
Identification of 1D-Signal Types Using Unsupervised Deep Learning
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.
bioinformatics, unsupervised learning, deep learning, autoencoders
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Podaci o izdanju
65
10.07.2017.
obranjeno
Podaci o ustanovi koja je dodijelila akademski stupanj
Fakultet elektrotehnike i računarstva
Zagreb