Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition
β Scribed by Takeyuki Tamura; Tatsuya Akutsu
- Book ID
- 115001021
- Publisher
- BioMed Central
- Year
- 2007
- Tongue
- English
- Weight
- 653 KB
- Volume
- 8
- Category
- Article
- ISSN
- 1471-2105
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## Abstract Support Vector Machine (SVM), which is one class of learning machines, was applied to predict the subcellular location of proteins by incorporating the quasiβsequenceβorder effect (Chou [2000] Biochem. Biophys. Res. Commun. 278:477β483). In this study, the proteins are classified into t
Given a protein sequence, how to identify its subcellular location? With the rapid increase in newly found protein sequences entering into databanks, the problem has become more and more important because the function of a protein is closely correlated with its localization. To practically deal with