CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders.

Chentianye Xu, Xueying Zhan, Min Xu

Abstract readPreprint

In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chentianye XuLanguage Technologies Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Xueying ZhanComputational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Min XuComputational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Funding

TrainingP41GM103712 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FAEDER, JAMES · 2012 to 2021

$16.3M

Novel machine learning approaches for improving structural discrimination in cryo-electron tomography-Administrative SupplementR01GM134020 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI XU, MIN · 2020 to 2023

$1.4M

NIGMS NIH HHS P41 GM103712NIGMS NIH HHS R01 GM134020

6 · The paper itself

Abstract

Cryo-electron microscopy (cryo-EM) emerges as a pivotal technology for determining the architecture of cells, viruses, and protein assemblies at near-atomic resolution. Traditional particle picking, a key step in cryo-EM, struggles with manual effort and automated methods' sensitivity to low signal-to-noise ratio (SNR) and varied particle orientations. Furthermore, existing neural network (NN)-based approaches often require extensive labeled datasets, limiting their practicality. To overcome these obstacles, we introduce

Indexed as

Cryo-electron microscopyfew-shot learningmasked autoencoderparticle picking

Identifiers

PMID38699171

PMCPMC11065045

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.