Naslov
Read classification using semi-supervised deep learning

Autori
Šebrek, Tomislav ; Tomljanović, Jan ; Krapac, Josip ; Šikić, Mile

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni

Izvornik
2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017 / - , 2017, 1-8

Skup
2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017

Mjesto i datum
Skopje, Sjeverna Makedonija, 18.07.2017. - 22.07.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
deep learning, Semi-supervised learning, De novo assembly, chimeric read, repeat read

Sažetak
N this paper, we propose a semi-supervised deep learning method for detecting the specific types of reads that impede the de novo genome assembly process. Instead of dealing directly with sequenced reads, we analyze their cov- erage graphs converted to 1D-signals. We noticed that specific signal patterns occur in each relevant class of reads. Semi-supervised approach is chosen be-cause manually labelling the data is a very slow and tedious process, so our goal was to facilitate the assembly process with as little labeled data as possible. We tested two models to learn patterns in the coverage graphs: M1+M2 and semi-GAN. We evaluated the performance of each model based on a manually labeled dataset that comprises various reads from multiple reference genomes with re-spect to the number of labeled examples that were used during the training pro-cess. In addition, we embedded our detection in the assembly process which im-proved the quality of assemblies.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo

POVEZANOST RADA