Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
Vise andre og tillknytning
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
HSV kategori
Forskningsprogram
biokemi med inriktning mot bioinformatik
Identifikatorer
URN: urn:nbn:se:su:diva-171426OAI: oai:DiVA.org:su-171426DiVA, id: diva2:1341022
Tilgjengelig fra: 2019-08-07 Laget: 2019-08-07 Sist oppdatert: 2019-12-12bibliografisk kontrollert
Inngår i avhandling
1. Predicting the route: from protein sequence to sorting in eukaryotic cell
Åpne denne publikasjonen i ny fane eller vindu >>Predicting the route: from protein sequence to sorting in eukaryotic cell
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Proteins need to be localised in the correct compartment of a eukaryotic cell to function correctly. Therefore, a protein needs to be transported to the right location. Specific signals present in the protein sequence direct proteins to different subcellular localisations. The correct transport is essential for the life of the cell, while, possible errors during the transport can cause irreversible damage and interfere with the activities of surrounding proteins. For more than 30 years, the development of methods to identify the localisation of proteins using both experimental and computational approaches has been an important research area. The objective of this thesis is to develop better computational methods for the classification of the subcellular localisation of eukaryotic proteins. I first describe the development of a consensus method, SubCons, which improves the subcellular prediction of human proteins. Next, I present the SubCons web-server as well as an additional benchmark using protein annotation from novel mass-spectrometry studies in two eukaryotic organisms Mus musculus and Drosophila melanogaster. Then, I present the new version of TargetP and how deep learning can improve the identification of N-terminal sorting signals by focusing on relevant biological signatures. Finally, I describe the development of a novel method for sub-nuclear localisation prediction. Here, I show that the performance of a deep convolutional neural network is improved when using an augmented dataset of homologous proteins.

sted, utgiver, år, opplag, sider
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. s. 65
Emneord
eukaryotic cell, sorting signals, subcellular localisation, machine learning, biological sequence analysis, bioinformatics
HSV kategori
Forskningsprogram
biokemi med inriktning mot bioinformatik
Identifikatorer
urn:nbn:se:su:diva-171434 (URN)978-91-7797-801-5 (ISBN)978-91-7797-802-2 (ISBN)
Disputas
2019-09-27, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (engelsk)
Opponent
Veileder
Merknad

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.

Tilgjengelig fra: 2019-09-04 Laget: 2019-08-08 Sist oppdatert: 2019-08-26bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Søk i DiVA

Av forfatter/redaktør
Salvatore, Marcovon Heijne, GunnarElofsson, Arne
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 153 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf