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Automatic Subcellular Protein Localization Using Deep Neural Networks
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatisk proteinlokalisering på subcellulär nivå med hjälp av djupa neurala nätverk (Swedish)
Abstract [en]

Protein localization is an important part in understanding the functionality of a protein. The current method of localizing proteins is to manually annotate microscopy images. This thesis investigates the feasibility of using deep artificial neural networks to automatically classify subcellular protein locations based on immunoflourescent images. We investigate the applicability in both single-label and multi-label classification, as well as cross cell line classification.

We show that deep single-label neural networks can be used for protein localization with up to 73% accuracy. We also show the potential of deep multi-label neural networks for protein localization and cross cell line classification but conclude that more research is needed before we can say for certain that the method is applicable.

Place, publisher, year, edition, pages
2016. , 42 p.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-189991OAI: diva2:950054
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2016-08-18 Created: 2016-07-27 Last updated: 2016-08-18Bibliographically approved

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Winsnes, Casper
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