ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction in

Dongbo Dai, Chenyang Huang, Junjie Li, Xiao Wei, Shengzhou Li, Qiong Wu, Huiran Zhang

Abstract read

In one paragraph

Article in Microorganisms, 2026. 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

7 authors.

Dongbo DaiSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Chenyang HuangSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Junjie LiSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Xiao WeiSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Shengzhou LiDepartment of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan.ORCID 0000-0001-6973-3825

Qiong WuDepartment of Laboratory Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.ORCID 0009-0002-8674-1370

Huiran ZhangSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.ORCID 0000-0002-9445-8930

Funding

Science and Technology Commission of Shanghai Municipal 24DX2800200

6 · The paper itself

Abstract

Rapid antimicrobial resistance (AMR) prediction from MALDI-TOF mass spectrometry (MS) remains challenging, particularly when training artificial intelligence (AI) models under small-sample constraints. Performance is often hampered by the high dimensionality of spectral data and the subtle nature of resistance-related signals: full-spectrum approaches risk overfitting to high-dimensional noise, whereas peak-selection strategies risk discarding structurally informative, low-intensity signals. Here, we propose ReShuffle-MS, a region-guided data augmentation framework for MS data. Each spectrum is partitioned into a Main Discriminative Region (MDR) and a Peripheral Peak Region (PPR). By recombining signals within the PPR across samples of the same class while keeping the MDR intact, ReShuffle-MS generates structure-preserving augmented samples. On a clinical dataset for

Indexed as

antimicrobial resistance (AMR)data augmentationdeep learningmachine learningmass spectrometry (MS)spectral recombination

Identifiers

PMID41597695

DOI10.3390/microorganisms14010177

PMCPMC12844117

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.