Evidence map›Paper›PMID 40241758›Full record

ArticleiScience2025

BERT-DomainAFP: Antifreeze protein recognition and classification model based on BERT and structural domain annotation.

Shengzhen Chen, Ping Zheng, Lele Zheng, Qinglong Yao, Ziyu Meng, Longshan Lin, Xinhua Chen, Ruoyu Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.Interdisciplinary sciences, computational life sciences · 2026
    Article
  2. 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

8 authors.

Shengzhen ChenState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Ping ZhengState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Lele ZhengState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Qinglong YaoState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Ziyu MengState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Longshan LinLaboratory of Marine Biodiversity Research, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China.
Xinhua ChenState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Ruoyu LiuState Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, Institute of Oceanology, College of Marine Sciences, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antifreeze proteins (AFPs) are crucial for organisms to adapt to low temperatures, with applications in medicine, food storage, aquaculture, and agriculture. Accurate AFP identification is challenging due to structural and sequence diversity. To improve prediction and classification, we propose BERT-DomainAFP, a deep learning model trained on the AntiFreezeDomains dataset created with a novel annotation strategy. The model uses pre-trained ProteinBERT and incorporates oversampling and undersampling techniques to handle unbalanced data, ensuring high predictive ability. BERT-DomainAFP achieves 98.48% accuracy, the highest among existing models, and can classify different AFP types based on structural domain features. This model outperforms current tools, offering a promising solution for AFP recognition and classification in research and applications.

Indexed as

BioinformaticsMolecular modellingProtein

Identifiers

PMID40241758
PMCPMC12002629

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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.