Evidence map›Paper›PMID 41124016›Full record

ArticleGigaScience2025

CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology.

Qibo Xu, Leon Wu, Michael Rebelo, Shi Feng, Xinye Yu, Farhanaz Farheen, Daisuke Kihara, Z Hong Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Qibo XuCalifornia NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0003-2084-3390
Leon WuCalifornia NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.ORCID 0009-0009-6227-8298
Michael RebeloCalifornia NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.ORCID 0009-0007-7732-9161
Shi FengCalifornia NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.ORCID 0009-0009-5468-9791
Xinye YuCalifornia NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-1764-1141
Farhanaz FarheenDepartment of Computer Science, Purdue University, West Lafayette, IN 47907, USA.ORCID 0009-0006-5683-6853
Daisuke KiharaDepartment of Computer Science, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0003-4091-6614
Z Hong ZhouCalifornia NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-8373-4717

Funding

High-Resolution CryoEM Reconstruction of Large ComplexesR01GM071940 · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2025 to 2025
$321k
National Science Foundation IIS2211598NIGMS NIH HHS R01 GM071940NIGMS NIH HHS R01 GM133840US National Institutes of Health R01GM071940 to Z.H.Z.US National Institutes of Health R01GM133840
6 · The paper itself

Abstract

Cryogenic electron microscopy (cryoEM) has revolutionized structural biology by enabling atomic-resolution visualization of biomacromolecules. With artificial intelligence (AI) increasing role in newly developed cryoEM tools, task-specific datasets have become essential. Yet assembling such datasets often demands considerable effort and domain expertise, constraining AI-driven cryoEM tool development efforts. Here, we present CryoDataBot, an automated pipeline that addresses this gap. CryoDataBot streamlines data retrieval, preprocessing, and labeling, with fine-grained quality control and flexible customization, enabling efficient generation of robust datasets. CryoDataBot's effectiveness is demonstrated through improved training efficiency in U-Net models and rapid, effective retraining of CryoREAD, a widely used RNA modeling tool. By simplifying the workflow and offering customizable quality control, CryoDataBot enables researchers to easily tailor dataset construction to the specific objectives of their models, while ensuring high data quality and reducing manual workload. This flexibility supports tools development for a wide range of applications in AI-driven structural biology.

Indexed as

Artificial IntelligenceComputational BiologyCryoelectron MicroscopySoftwareautomated data curationcryogenic electron microscopy (cryoEM)dataset generation and validationdeep learningquality assessment (Q-score, VOF)structural biology

Identifiers

PMID41124016
PMCPMC12596181

What Socratic 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 Socratic graph.