Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat 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.
Visa övriga samt affilieringar
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Forskningsämne
biokemi med inriktning mot bioinformatik
Identifikatorer
URN: urn:nbn:se:su:diva-171426OAI: oai:DiVA.org:su-171426DiVA, id: diva2:1341022
Tillgänglig från: 2019-08-07 Skapad: 2019-08-07 Senast uppdaterad: 2019-08-19Bibliografiskt granskad
Ingår i avhandling
1. Predicting the route: from protein sequence to sorting in eukaryotic cell
Öppna denna publikation i ny flik eller fönster >>Predicting the route: from protein sequence to sorting in eukaryotic cell
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. s. 65
Nyckelord
eukaryotic cell, sorting signals, subcellular localisation, machine learning, biological sequence analysis, bioinformatics
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Forskningsämne
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)
Disputation
2019-09-27, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript.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

Tillgänglig från: 2019-09-04 Skapad: 2019-08-08 Senast uppdaterad: 2019-08-26Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Sök vidare i DiVA

Av författaren/redaktören
Salvatore, Marcovon Heijne, GunnarElofsson, Arne
Av organisationen
Institutionen för biokemi och biofysik
Bioinformatik (beräkningsbiologi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 73 träffar
RefereraExporteraLänk till posten
Permanent länk

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