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Vullo, A. and Passerini, A. (2004) Disulfide Connectivity Prediction Using Recursive Neural Networks and Evolutionary Information. Bioinformatics, 20, 653-659. http://dx.doi.org/10.1093/bioinformatics/btg463

has been cited by the following article:

• TITLE: A Prediction Method of Protein Disulfide Bond Based on Hybrid Strategy

  AUTHORS: Pengfei Sun, Yunhong Ding, Yuyan Huang, Lei Zhang

  KEYWORDS: Disulfide Bond, Support Vector Machine, Sample Selection

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.9 No.10B, September 23, 2016

  ABSTRACT: A prediction method of protein disulfide bond based on support vector machine and sample selection is proposed in this paper. First, the protein sequences selected are en-coded according to a certain encoding, input data for the prediction model of protein disulfide bond is generated; Then sample selection technique is used to select a portion of input data as training samples of support vector machine; finally the prediction model training samples trained is used to predict protein disulfide bond. The result of simulation experiment shows that the prediction model based on support vector ma-chine and sample selection can increase the prediction accuracy of protein disulfide bond.