Share This Article:

Visualization of protein structure relationships using constrained twin kernel embedding

Abstract Full-Text HTML Download Download as PDF (Size:388KB) PP. 133-140
DOI: 10.4236/jbise.2008.12022    3,980 Downloads   7,401 Views   Citations


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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Guo, Y. , Gao, J. , Kwan, P. and Hou, X. (2008) Visualization of protein structure relationships using constrained twin kernel embedding. Journal of Biomedical Science and Engineering, 1, 133-140. doi: 10.4236/jbise.2008.12022.


[1] Dimitris K. Agrafiotis. (1997) A new method for analyzing protein sequence relationships based on sammon maps. Protein Science, 6(2):287–293.
[2] Izydor Apostol and Wojciech Szpankowski. (1999) Indexing and mapping of proteins using a modified nonlinear sammon projection. Journal of Computational Chemistry, 20 (10):1049– 1059.
[3] Mikhail Belkin and Partha Niyogi. (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computa-tion, 15(6):1373–1396.
[4] Samarasena Buchala, Neil Davey, Ray J. Frank, and Tim M. Gale. (2004) Dimensionality reduction of face images for gender classifi-cation. In Proceedings of 2nd International IEEE Conference on Intelligent Systems, volume 1, pages 88–93.
[5] Payel Das, Mark Moll, Hern an Stamati, Lydia E. Kavraki, and Cecilia Clementi. (2006) Lowdimensional free energy landscapes of protein folding reactions by nonlinear dimensionality reduction. In Proceedings of the National Academy of Sciences, volume 103, pages 9885?9890, USA
[6] Michael A. Farnum, Huafeng Xu, and Dimitris K. Agrafiotis. (2003) Exploring the nonlinear geometry of protein homology. Protein Science, 12(1604-1612) .
[7] Ronald A. Fisher. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179–188 .
[8] Amir Globerson, Gal Chechik, Fernando Pereira, and Naftali Tishby.(2005) Euclidean embedding of co-occurrence data. In Lawrence K. Saul, YairWeiss, and L eon Bottou, editors, Advances in Neural Information Processing Systems 17, pages 497?04. MIT Press, Cambridge, MA .
[9] Yi Guo, Junbin Gao, and Paul W. Kwan. (2006) Kernel Laplacian eigenmaps for visualization of non-vectorial data. In Lecture Notes on Artificial Intelligence, volume 4304, pages 1179– 1183.
[10] Yi Guo, Junbin Gao, and Paul W. Kwan. (2008) Twin kernel embedding. IEEE Transaction ofPattern Analysis and Machine Intelligence, submitted.
[11] Jihun Ham, Yuanqing Lin, and Daniel. D. Lee. (2005) Learning nonlinear appearance manifolds for robot localization. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2971– 2976.
[12] Jens Hanke and Jens G. Reich. (1996) Kohonen map as a visuali-zation tool for the analysis of protein sequences: multiple align-ments, domains and segments of secondary structures. Bioinfor-matics, 12(6):447–454.
[13] M. Jolliffe. (1986) Principal Component Analysis. Springer-Verlag, New York.
[14] Philip M. Kim and Bruce Tidor. (2003) Subsystem identification through dimensionality reduction of large-scale gene expression data. Genome Research, 13:1706–1718.
[15] Nathan Mekuz, Christian Bauckhage, and John K. Tsotsos. (2005) Face recognition with weighted locally linear embedding. In Pro-ceedings of the 2nd Canadian Conference on Computer and Ro-bot Vision, pages 290–296.
[16] Oleg Okun, Helen Priisalu, and Alexessander Alves. (2005) Fast non-negative dimensionality reduction for protein fold recognition. In ECML, pages 665–672.
[17] Zhengjun Pan, Rod Adams, and Hamid Bolouri. (2000) Dimen-sionality reduction of face images using discrete cosine transforms for recognition. In IEEE Conference on Computer Vision and Pattern Recognition. [18] Jian Qiu, Martial Hue, Asa Ben-Hur, Jean-Philippe Vert, and William Stafford Noble. (2007) An alignment kernel for protein structures. In Bioinformatics.
[18] Bisser Raytchev, Ikushi Yoda, and Katsuhiko Sakaue. (2006) Multi-view face recognition by nonlinear dimensionality reduc-tion and generalized linear models. In FGR ’06: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pages 625–630, Washington, DC, USA.
[19] Sam T. Roweis and Lawrence K. Saul. (2000) Nonlinear dimen-sionality reduction by locally linear embedding. Science, 290(22):2323–2326.
[20] B. Sch¨olkopf and A.J. Smola. (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Be-yond. The MIT Press, Cambridge, MA.
[21] Bernhard Sch¨olkopf, Alexander J. Smola, and Klaus-Robert M¨uller. (1998) Nonlinear component analysis as a kernel eigen-value problem. Neural Computation, 10:1299–1319.
[22] J.A.K. Suykens and J. Vandewalle. (1999) Least squares support vector machine classifiers. Neural Processing Letters, 9:293–300.
[23] Johan A.K. Suykens. (2007) Data visualization and dimensional-ity reduction using kernel maps with a reference point. Technical Report 07-22, K.U. Leuven, ESAT-SCD/SISTA.
[24] Joshua B. Tenenbaum, Vin de Silva, and John C. Langford. (2000) A global geometric framework for nonlinear dimensionality re-duction. Science, 290(22):2319–2323.
[25] Miguel L. Teodoro, George N. Phillips Jr, and Lydia E. Kavraki. (2002) A dimensionality reduction approach to modeling protein flexibility. In International Conference on Computational Mo-lecular Biology (RECOMB), pages 299–308.
[26] Miguel L. Teodoro, George N. Phillips Jr, and Lydia E. Kavraki. (2003) Understanding protein flexibility through dimensionality reduction. Journal of Computational Biology, 10(3-4):617–634

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.