Shape component analysis: structure-preserving dimension reduction on biological shape spaces | Zendy

Hao-Chih Lee | Zendy; Tao Liao | Zendy; Yongjie Zhang | Zendy; Ge Yang | Zendy

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Shape component analysis: structure-preserving dimension reduction on biological shape spaces

Author(s) -

Hao-Chih Lee,

Tao Liao,

Yongjie Zhang,

Ge Yang

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv648

Subject(s) - computer science , cluster analysis , dimensionality reduction , dimension (graph theory) , shape analysis (program analysis) , matlab , reduction (mathematics) , biological data , dimensional reduction , component (thermodynamics) , high dimensional , pattern recognition (psychology) , theoretical computer science , artificial intelligence , mathematics , geometry , static analysis , bioinformatics , physics , thermodynamics , pure mathematics , mathematical physics , biology , programming language , operating system

Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques.

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