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A novel representation of protein sequences for prediction of subcellular location using support vector machines

✍ Scribed by Setsuro Matsuda; Jean-Philippe Vert; Hiroto Saigo; Nobuhisa Ueda; Hiroyuki Toh; Tatsuya Akutsu

Book ID
111752548
Publisher
Cold Spring Harbor Laboratory Press
Year
2005
Tongue
English
Weight
380 KB
Volume
14
Category
Article
ISSN
0961-8368

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Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect
✍ 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