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A multi-label classifier for prediction membrane protein functional types in animal.
- Source :
-
The Journal of membrane biology [J Membr Biol] 2014 Nov; Vol. 247 (11), pp. 1141-8. Date of Electronic Publication: 2014 Aug 09. - Publication Year :
- 2014
-
Abstract
- Membrane protein is an important composition of cell membrane. Given a membrane protein sequence, how can we identify its type(s) is very important because the type keeps a close correlation with its functions. According to previous studies, membrane protein can be divided into the following eight types: single-pass type I, single-pass type II, single-pass type III, single-pass type IV, multipass, lipid-anchor, GPI-anchor, peripheral membrane protein. With the avalanche of newly found protein sequences in the post-genomic age, it is urgent to develop an automatic and effective computational method to rapid and reliable prediction of the types of membrane proteins. At present, most of the existing methods were based on the assumption that one membrane protein only belongs to one type. Actually, a membrane protein may simultaneously exist at two or more different functional types. In this study, a new method by hybridizing the pseudo amino acid composition with multi-label algorithm called LIFT (multi-label learning with label-specific features) was proposed to predict the functional types both singleplex and multiplex animal membrane proteins. Experimental result on a stringent benchmark dataset of membrane proteins by jackknife test show that the absolute-true obtained was 0.6342, indicating that our approach is quite promising. It may become a useful high-through tool, or at least play a complementary role to the existing predictors in identifying functional types of membrane proteins.
- Subjects :
- Amino Acid Sequence
Membrane Proteins metabolism
Molecular Sequence Data
Structure-Activity Relationship
Algorithms
Artificial Intelligence
Membrane Proteins chemistry
Membrane Proteins classification
Pattern Recognition, Automated methods
Sequence Alignment methods
Sequence Analysis, Protein methods
Subjects
Details
- Language :
- English
- ISSN :
- 1432-1424
- Volume :
- 247
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- The Journal of membrane biology
- Publication Type :
- Academic Journal
- Accession number :
- 25107302
- Full Text :
- https://doi.org/10.1007/s00232-014-9708-2