A pseudo-labeling approach for automated assignment of unlabeled proteins exceeding 100 amino acid residues.

Xiaogang Niu, Xianhui Hou, Changwen Jin

Abstract read

In one paragraph

Article in Magnetic resonance letters, 2027. 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.

Xiaogang NiuBeijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.

Xianhui HouBeijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.

Changwen JinBeijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the era of AI-driven protein structure prediction, there is an urgent demand for rapid, accessible experimental approaches to validate computationally predicted structural models or to improve the accuracy of AI-based structure determination. As a key experimental technique for determining protein structures in solution, conventional biomolecular NMR spectroscopy relies heavily on proteins labeled with

Indexed as

Artificial intelligenceAutomated assignmentIsotope labellingProtein NMR

Identifiers

PMID42872102

DOI10.1016/j.mrl.2026.200309

PMCPMC13660641

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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.