{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T08:40:12Z","timestamp":1718181612422},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: 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.<\/jats:p><jats:p>Results: We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins.<\/jats:p><jats:p>Availability and implementation: Source code and data for reproducing results of this article are freely available at https:\/\/github.com\/ccdlcmu\/shape_component_analysis_Matlab. The implementation was made in MATLAB and supported on MS Windows, Linux and Mac OS.<\/jats:p><jats:p>Contact: \u00a0geyang@andrew.cmu.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv648","type":"journal-article","created":{"date-parts":[[2015,11,6]],"date-time":"2015-11-06T01:34:15Z","timestamp":1446773655000},"page":"755-763","source":"Crossref","is-referenced-by-count":3,"title":["Shape component analysis: structure-preserving dimension reduction on biological shape spaces"],"prefix":"10.1093","volume":"32","author":[{"given":"Hao-Chih","family":"Lee","sequence":"first","affiliation":[{"name":"1 Department of Biomedical Engineering,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liao","sequence":"additional","affiliation":[{"name":"2 Department of Mechanical Engineering, and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjie Jessica","family":"Zhang","sequence":"additional","affiliation":[{"name":"1 Department of Biomedical Engineering,"},{"name":"2 Department of Mechanical Engineering, and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Yang","sequence":"additional","affiliation":[{"name":"1 Department of Biomedical Engineering,"},{"name":"3 Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,11,4]]},"reference":[{"key":"2023020110452128500_btv648-B1","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","article-title":"Laplacian eigenmaps for dimensionality reduction and data representation","volume":"15","author":"Belkin","year":"2003","journal-title":"Neural Comput."},{"key":"2023020110452128500_btv648-B2","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/76.927426","article-title":"MPEG-7 visual shape descriptors","volume":"11","author":"Bober","year":"2001","journal-title":"IEEE Trans. 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