Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for uns...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Julkaisussa:PLoS One
Päätekijät: Wu, Jiayi, Ma, Yong-Bei, Congdon, Charles, Brett, Bevin, Chen, Shuobing, Xu, Yaofang, Ouyang, Qi, Mao, Youdong
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Public Library of Science 2017
Aiheet:
Research Article
Linkit:https://ncbi.nlm.nih.gov/pmc/articles/PMC5546606/
https://ncbi.nlm.nih.gov/pubmed/28786986
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0182130
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