Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Comparing approaches to predict transmembrane domains in protein sequences
Blekinge Institute of Technology, Sweden.
Travelstart Nordic, Sweden.ORCID iD: 0000-0002-8591-1035
Ericsson, Sweden.
2005 (English)In: ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing, ACM Press, 2005, 185-189 p.Conference paper, Published paper (Refereed)
Abstract [en]

There are today several systems for predicting transmembrane domains in membrane protein sequences. As they are based on different classifiers as well as different pre- and post-processing techniques, it is very difficult to evaluate the performance of the particular classifier used. We have developed a system called MemMiC for predicting transmembrane domains in protein se-quences with the possibility to choose between different ap-proaches to pre- and post-processing as well as different classifiers. Therefore it is possible to compare the performance of each classifier in a certain environment as well as the different approaches to pre- and post-processing. We have demonstrated the usefulness of MemMiC in a set of experiments, which shows, e.g., that the performance of a classifier is very dependent on which pre- and post-processing techniques are used.

Place, publisher, year, edition, pages
ACM Press, 2005. 185-189 p.
Keyword [en]
learning, classifiers, protein sequences
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-42364DOI: 10.1145/1066677.1066720ISBN: 1-58113-964-0 (print)OAI: oai:DiVA.org:lnu-42364DiVA: diva2:805245
Conference
2005 ACM Symposium on Applied computing
Available from: 2015-04-15 Created: 2015-04-15 Last updated: 2015-09-17Bibliographically approved

Open Access in DiVA

fulltext(302 kB)30 downloads
File information
File name FULLTEXT02.pdfFile size 302 kBChecksum SHA-512
0c60e4b4769a0bac4f17b4a55bc71016ec856883288413bdd5c598a7ebc86d8ffe7dc78dba495be5f76556fd97cc8a3b09b51e472b33c7d0235c5e242a2eaec9
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Hagelbäck, Johan
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 30 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 39 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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