𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

✍ 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

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Support vector machines for prediction o
✍ Yu-Dong Cai; Xiao-Jun Liu; Xue-biao Xu; Kuo-Chen Chou πŸ“‚ Article πŸ“… 2002 πŸ› John Wiley and Sons 🌐 English βš– 124 KB

## Abstract Support Vector Machine (SVM), which is one class of learning machines, was applied to predict the subcellular location of proteins by incorporating the quasi‐sequence‐order effect (Chou [2000] Biochem. Biophys. Res. Commun. 278:477–483). In this study, the proteins are classified into t

Prediction and classification of protein
✍ Kuo-Chen Chou; Yu-Dong Cai πŸ“‚ Article πŸ“… 2003 πŸ› John Wiley and Sons 🌐 English βš– 206 KB

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