SwePub - sökning: L773:1581139640

Numrering	Referens	Omslagsbild	Hitta
1.	Davidsson, Paul, et al. (författare) Comparing approaches to predict transmembrane domains in protein sequences 2005 Ingår i: ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing. - New York, NY, USA : ACM Press. - 1581139640 ; , s. 185-189 Konferensbidrag (refereegranskat)abstract 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.

Davidsson, Paul, et al. (författare)
Comparing approaches to predict transmembrane domains in protein sequences
2005
Ingår i: ProceedingSAC '05 Proceedings of the 2005 ACM symposium on Applied computing. - New York, NY, USA : ACM Press. - 1581139640 ; , s. 185-189
Konferensbidrag (refereegranskat)abstract
- 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.

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