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Ding, C. H. and Dubchak, I. (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17, 349-358.

has been cited by the following article:

• TITLE: Prediction of protein folding rates from primary sequence by fusing multiple sequential features

  AUTHORS: Hong-Bin Shen, Jiang-Ning Song, Kuo-Chen Chou

  KEYWORDS: Protein Folding Rate; Ensemble Predictor; Fusion Approach; Web-Server; Pred-PFR

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.3, May 27, 2009

  ABSTRACT: We have developed a web-server for predicting the folding rate of a protein based on its amino acid sequence information alone. The web- server is called Pred-PFR (Predicting Protein Folding Rate). Pred-PFR is featured by fusing multiple individual predictors, each of which is established based on one special feature derived from the protein sequence. The ensemble pre-dictor thus formed is superior to the individual ones, as demonstrated by achieving higher correlation coefficient and lower root mean square deviation between the predicted and observed results when examined by the jack-knife cross-validation on a benchmark dataset constructed recently. As a user-friendly web- server, Pred-PFR is freely accessible to the public at www.csbio.sjtu.edu.cn/bioinf/Folding Rate/.