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Prediction of peptide retention time based on Gaussain Processes
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Shotgun Proteomics is the leading technique for protein identification in complexmixtures. However, it produces a large amount of data which results in aextremely high computational cost for identifying the protein. Retention time(RT) is an important factor to be used to enhance the efficiency of protein identification.By predicting the retention time successfully, we could decrease thecomputational cost dramatically. This thesis uses a machine learning method,Gaussian Processes, to predict the retention time of a set of peptide in hand.We also implement a feature extraction method called Bag-of-Words to generatethe features for training the model. In addition, we also investigate theeffect of different types of optimization methods to the model’s parameters.The results show comparable precision of the prediction and relatively lowtime cost when comparing with the state-of-art prediction model.

Place, publisher, year, edition, pages
2015. , 46 p.
Keyword [en]
Peptide, retention time, gaussian processes
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-175982OAI: oai:DiVA.org:kth-175982DiVA: diva2:864254
Supervisors
Examiners
Available from: 2015-10-27 Created: 2015-10-26 Last updated: 2015-10-27Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf