Identifikacija tipova 1D-signala pomoću polu-nadziranog dubokog učenja

master's thesis

• Title (english): Classification of 1D-Signal Types Using Semi-Supervised Deep Learning
• Author: Tomislav Šebrek
• Mentor: Mile Šikić (mentor)
• Committee member: Mile Šikić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Krešimir Križanović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-10, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U okviru ovog rada, predložili smo metode za detekciju tipova grafova pokrivenosti na temelju polu-nadziranih modela dubokog učenja: autoenkodera i generativnih suparničkih mreža. Testirali smo performanse svih modela koristeći bazu koja sadržava očitanja s više referentnih genoma. Ručno smo označili dio podataka i usporedili rezultate s obzirom na količinu označenih primjera koji su bili korišteni prilikom treniranja. Ovu detekciju smo ugradili u proces sastavljanja i dobili dobre rezultate. Izvorni kod je dostupan na https://github.com/tomislavsebrek/diplomski.

Abstract (english)

In this thesis, we proposed methods for detecting the type of the coverage graph based on semi-supervised deep learning models: autoencoders and generative adversarial networks. We evaluated the performance of each model based on the dataset that contained reads from multiple reference genomes. We have manually labeled some of the data and compared the results of all models with respect to the number of labeled examples that were used during the training process. We have embedded this detection in the assembly process and achieved good results. The source code is available at https://github.com/tomislavsebrek/diplomski.

• Keywords: duboko učenje; autoenkoder; generativni suparnički model; polu-nadzirano učenje; graf pokrivenosti; kimerno očitanje; ponavljajuće očitanje
• Keywords (english): deep learning; autoencoder; generative adversarial network; semi-supervised learning; coverage graph; chimeric read; repeat read
• Language: english
• URN:NBN: urn:nbn:hr:168:377071
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Repository: FER Repository
• Created on: 2019-03-14 01:10:11