✦ LIBER ✦

Support Vector Machines for Prediction of Protein Domain Structural Class

✍ Scribed by YU-DONG CAI; XIAO-JUN LIU; XUE-BIAO XU; KUO-CHEN CHOU

Publisher: Elsevier Science
Year: 2003
Tongue: English
Weight: 126 KB
Volume: 221
Category: Article
ISSN: 0022-5193
DOI: 10.1006/jtbi.2003.3179

No coin nor oath required. For personal study only.

✦ Synopsis

The support vector machines (SVMs) method was introduced for predicting the structural class of protein domains. The results obtained through the self-consistency test, jack-knife test, and independent dataset test have indicated that the current method and the elegant component-coupled algorithm developed by Chou and co-workers, if effectively complemented with each other, may become a powerful tool for predicting the structural class of protein domains.

📜 SIMILAR VOLUMES

Support Vector Machines for Protein Fold

Support Vector Machines for Protein Fold Class Prediction

✍ Florian Markowetz; Lutz Edler; Martin Vingron 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 109 KB 👁 2 views

Using support vector machines for predic

Using support vector machines for prediction of protein structural classes based on discrete wavelet transform

✍ Jian-Ding Qiu; San-Hua Luo; Jian-Hua Huang; Ru-Ping Liang 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 211 KB

## Abstract The prediction of secondary structure is a fundamental and important component in the analytical study of protein structure and functions. How to improve the predictive accuracy of protein structural classification by effectively incorporating the sequence‐order effects is an important

Support vector machines for predicting H

Support vector machines for predicting HIV protease cleavage sites in protein

✍ Yu-Dong Cai; Xiao-Jun Liu; Xue-Biao Xu; Kuo-Chen Chou 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 96 KB

## Abstract Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. The pace in searching for the proper inhibitors of HIV protease will be

Support vector machines for prediction o

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 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 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

Substructure-Based Support Vector Machin

Substructure-Based Support Vector Machine Classifiers for Prediction of Adverse Effects in Diverse Classes of Drugs.

✍ S. Bhavani; A. Nagargadde; A. Thawani; V. Sridhar; N. Chandra 📂 Article 📅 2007 🏛 John Wiley and Sons ⚖ 11 KB 👁 2 views

Prediction of function changes associate

Prediction of function changes associated with single-point protein mutations using support vector machines (SVMs)

✍ Shan Gao; Ning Zhang; Guang You Duan; Zhuo Yang; Ji Shou Ruan; Tao Zhang 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 190 KB

Computational methods can be used to predict the effects of single amino acid substitutions (single-point mutations). In contrast to previous methods that need many protein sequence and structural features, we applied support vector machines (SVMs) to predict protein function changes associated with