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Mikhail Belkin and Partha Niyogi. (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computa-tion, 15(6):1373–1396.

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• TITLE: Visualization of protein structure relationships using constrained twin kernel embedding

  AUTHORS: Yi Guo, Jun-Bin Gao, Paul Kwan, Xinsheng Hou

  KEYWORDS: Visualization of protein structure, Constrained twin kernel embedding

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.1 No.2, August 25, 2008

  ABSTRACT: In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.