Evidence map›Paper›PMID 38303023›Full record

ArticleGenome biology2024

AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding.

Lingyan Zheng, Shuiyang Shi, Mingkun Lu, Pan Fang, Ziqi Pan, Hongning Zhang, Zhimeng Zhou, Hanyu Zhang, Minjie Mou, Shijie Huang and 8 more

Abstract read
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

18 authors.

Lingyan Zheng *College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Shuiyang Shi *College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Mingkun Lu *College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Pan FangIndustry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
Ziqi PanCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Hongning ZhangCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Zhimeng ZhouCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Hanyu ZhangCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Minjie MouCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Shijie HuangCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
Lin TaoKey Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Weiqi XiaPharmaceutical Department, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China.
Honglin LiSchool of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
Zhenyu ZengIndustry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
Shun ZhangIndustry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
Yuzong ChenState Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China.
Zhaorong LiIndustry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China. zhaorong.lzr@alibaba-inc.com.
Feng ZhuCollege of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China. zhufeng@zju.edu.cn.ORCID 0000-0001-8069-0053

Funding

National Natural Science Foundation of China 22220102001National Natural Science Foundation of China 81872798National Natural Science Foundation of China 82373790National Natural Science Foundation of China U1909208
6 · The paper itself

Abstract

Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO families containing few annotated proteins. Herein, an innovative strategy named AnnoPRO was therefore constructed by enabling sequence-based multi-scale protein representation, dual-path protein encoding using pre-training, and function annotation by long short-term memory-based decoding. A variety of case studies based on different benchmarks were conducted, which confirmed the superior performance of AnnoPRO among available methods. Source code and models have been made freely available at: https://github.com/idrblab/AnnoPRO and https://zenodo.org/records/10012272.

Indexed as

Deep LearningComputational BiologyHumansMolecular Sequence AnnotationProteinsSoftwareProteinsLong-tail problemLSTMPre-trainingProtein function annotationProtein representation

Identifiers

PMID38303023
PMCPMC10832132

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Registered trials

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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.