Naslov
Deep Learning Model of Nanopore Sequencing Pore

Autori
Penić, Rafael Josip

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

Mjesto
Zargeb

Datum
09.07

Godina
2021

Stranica
42

Mentor
Šikić, Mile

Neposredni voditelj
Stanojević, Dominik

Ključne riječi
bioinformatics ; self-supervised learning ; sequencing ; nanopore ; DNA ; deep learning ; machine learning

Sažetak
Nanopore sequencing is one of the most modern sequencing technologies and in this thesis we explored how do the self-supervised methods affect models that work with nanopore readings. We tried out contrastive predictive coding method with which we tried to predict signal's future latent representations. Sadly, this approach did not improve model's performance on the downstream task. We achieved best results with self- supervised representation learning methods which are very popular in the field of computer vision. Signal augmentations have a very important role in these algorithms and it is very important to think of good augmentations that will help model learn high- quality signal representations.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA