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A Bayesian adaptive basis algorithm for single particle reconstruction.

Abstract：

:Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. This paper proposes a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background.

journal_name

J Struct Biol

journal_title

Journal of structural biology

authors

Kucukelbir A,Sigworth FJ,Tagare HD

doi

10.1016/j.jsb.2012.04.012

subject

Has Abstract

pub_date

2012-07-01 00:00:00

pages

56-67

issue

1

eissn

1047-8477

issn

1095-8657

pii

S1047-8477(12)00115-3

journal_volume

179

pub_type

杂志文章

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