Evidence map›Paper›PMID 42015180›Full record

ArticleGenome biology2026

Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.

Jian Song, Amanda Momenzadeh, Hebin Liu, Chengpin Shen, Jesse G Meyer, Xiaohui Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Jian SongCancer Institute, Suzhou Medical College, Soochow University, Suzhou, 215000, China. songjian2022@suda.edu.cn.
Amanda MomenzadehDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
Hebin LiuShanghai Omicsolution Co., Ltd., Shanghai, 200000, China.
Chengpin ShenShanghai Omicsolution Co., Ltd., Shanghai, 200000, China. issac.shen@omicsolution.com.
Jesse G MeyerDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA. Jesse.Meyer@cshs.org.
Xiaohui WuCancer Institute, Suzhou Medical College, Soochow University, Suzhou, 215000, China. xhwu@suda.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

diaPASEF improves ion utilization and sensitivity by synchronizing quadrupole isolation with trapped ion mobility separation, making it suitable for single-cell proteomics. We present Full-DIA, a deep learning-driven software that enhances proteome coverage, quantitative accuracy, and analysis speed over DIA-NN for single-cell diaPASEF data. Notably, Full-DIA generates a missing-value-free protein matrix under stringent global FDR control, enabling downstream analyses without data gaps. Applied to LPS-treated and cell-cycle datasets, this matrix yields pathway enrichment results with fewer off-target and more biologically relevant pathways. Full-DIA highlights the potential of deep learning for four-dimensional diaPASEF analysis and offers a solution to missing values.

Indexed as

Deep LearningProteomeProteomicsSingle-Cell AnalysisSoftwareProteome

Identifiers

PMID42015180
PMCPMC13231635

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.