University of Zagreb. Faculty of Electrical Engineering and Computing.
Abstract
Cilj ovog rada bio je implementirati model temeljen na dubokom uˇcenju koji bi
pove´cao toˇcnost sastavljenog genoma. Konstruirani model temelji se na arhitekturi
rezidualne neuronske mreže te postiže toˇcnost ve´cu od trenutno najboljih alata temeljenih
na klasiˇcnom raˇcunarstvu, no zaostaje ne nadmašuje najbolje alate temeljene na
dubokom ucˇenju. Med¯utim, zbog ogranicˇenja u vidu prostora za pohranu podataka i
vremena treniranja modela, detaljnija analiza tek treba biti napravljena za više bakterijskih
uzoraka i razliˇcite arhitekture mreže.Goal of this work was to implement a deep learning model in order to increase
the accuracy of the assembled genome. This model is based on a residual network
architecture and achieves higher accuracy than classical state-of-the-art tools, but falls
behind the best deep-learning tools available. However, due to limitations in terms
of storage and time for training the model, further analysis has to be conducted for
different bacteria datasets and network architectures