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G. Wu and S. Yan, (2007) Improvement of prediction of mutation positions in H5N1 hemagglutinins of influenza A virus using neural network with distinguishing of arginine, leucine and serine, Protein Pept. Lett., 14, 465-470.

has been cited by the following article:

• TITLE: Descriptively probabilistic relationship between mutated primary structure of von Hippel-Lindau protein and its clinical outcome

  AUTHORS: Shao-Min Yan, Guang Wu

  KEYWORDS: Amino Acid; Bayes’ Law; Cross-Impact Analysis; Distribution Probability; Mutation; Von Hip-pel-Lindau Disease

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

  ABSTRACT: In this study, we use the cross-impact analysis to build a descriptively probabilistic relationship between mutant von Hippel-Lindau protein and its clinical outcome after quantifying mutant von Hippel-Lindau proteins with the amino-acid distribution probability, then we use the Bayes-ian equation to determine the probability that the von Hippel-Lindau disease occurs under a mutation, and finally we attempt to distinguish the classifications of clinical outcomes as well as the endocrine and nonendocrine neoplasia induced by mutations of von Hippel-Lindau protein. The results show that a patient has 9/10 chance of being von Hippel-Lindau disease when a new mutation occurs in von Hippel- Lindau protein, the possible distinguishing of classifications of clinical outcomes using mod-eling, and the explanation of the endocrine and nonendocrine neoplasia in modeling view.