Evidence map›Paper›PMID 40491374›Full record

ReviewCurrent protein & peptide science2026

Applications and Prospects of Artificial Intelligence in Proteomics

Yun Shao, Chenghui Yang, Shenhuan Ni, Mingwei Pang, Xiaojie Liu, Ren Kong, Shan Chang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current protein & peptide science, 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. Review
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.

Yun ShaoInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
Chenghui YangInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
Shenhuan NiInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
Mingwei PangInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
Xiaojie LiuInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
Ren KongInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
Shan ChangInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.

Funding

National Natural Science Foundation of China 62373172
6 · The paper itself

Abstract

Proteomics holds immense significance in fundamental and applied research in various fields, including life sciences, medicinal sciences, and pharmaceutical sciences. The rapid development of mass spectrometry (MS) technologies has facilitated MS-based proteomics research, which has emerged as one of the primary methods for determining the composition, structures, and functions of proteins. The necessity of processing these complex datasets has increased significantly owing to the growing volume and diversity of MS data pertaining to proteins. Artificial intelligence (AI) possesses powerful data processing abilities, and is being increasingly employed for handling these challenges. In particular, deep learning has been extensively employed in MSbased proteomics research. This review discusses and compares the different AI algorithms developed for various tasks, including the prediction of protein spectra, retention times, peptide sequences, and MS-based protein structure prediction, and highlights their respective strengths and weaknesses. The limitations and future prospects of AI in MS-based proteomics research are additionally discussed herein.

Indexed as

Artificial IntelligenceMass SpectrometryProteinsProteomicsAlgorithmsDatabases, ProteinDeep LearningHumansPeptidesPeptidesProteinsalgorithmsdatasetsDeep learningpeptide sequenceprotein spectraprotein structure predictionretention time

Identifiers

PMID40491374

What OpenQuestion holds

Textmetadata
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.