Search: onr:"swepub:oai:DiVA.org:lnu-42364" >
Comparing approache...
Comparing approaches to predict transmembrane domains in protein sequences
-
- Davidsson, Paul (author)
- Blekinge Tekniska Högskola,Sektionen för datavetenskap och kommunikation
-
- Hagelbäck, Johan, 1977- (author)
- Travelstart Nordic, Sweden,Travelstart Nordic, SWE
-
- Svensson, Kenny (author)
- Ericsson, Sweden,Ericsson AB, SWE
-
(creator_code:org_t)
- 2005-03-13
- 2005
- English.
-
In: ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing. - New York, NY, USA : ACM Press. - 1581139640 ; , s. 185-189
- Related links:
-
https://lnu.diva-por... (primary) (Raw object)
-
show more...
-
http://bth.diva-port...
-
https://bth.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
show less...
Abstract
Subject headings
Close
- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- learning
- classifiers
- protein sequences
- Computer Science
- Datavetenskap
Publication and Content Type
- ref (subject category)
- kon (subject category)
Find in a library
To the university's database