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Visualizing Human Genes on Manifolds Embedded in Three-Dimensional Space

  • Wei-Chen Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7197)

Abstract

This work provides a visualization tool for researchers to explore the geometry of the distribution of human protein-coding DNA in three-dimensional space by applying various manifold learning techniques, which preserve distinct relations among genes. The simulations suggest that the relations hidden among genetic sequences could be explored by the manifolds embedded in Euclidean space. Operating this software, users are able to rotate, scale and shift the three-dimensional spaces in an interactive manner.

Keywords

Manifold Learning Longe Common Subsequence Wellcome Trust Sanger Institute Manifold Space Inverse Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wei-Chen Cheng
    • 1
  1. 1.Institute of Statistical ScienceAcademia SinicaRepublic of China

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