engleski

Classification of 1D-Signal Types Using Deep Learning

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.

bioinformatics, sequence assembly, false overlaps, deep learning

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