Evidence map›Paper›PMID 38275064›Full record

ReviewCurrent medicinal chemistry2024

Wenzhen Li, Jinyi Wei, Qianhu Jiang, Yuwei Zhou, Xingru Yan, Changcheng Xiang, Jian Huang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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

7 authors.

Wenzhen LiSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Jinyi WeiSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Qianhu JiangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Yuwei ZhouSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Xingru YanSchool of Biological Sciences, University of Auckland, Auckland, New Zealand.
Changcheng XiangSchool of Computer Science and Technology, Aba Teachers University, Aba, Sichuan, China.ORCID 0009-0008-3781-9884
Jian HuangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.ORCID 0000-0003-3282-8892

Funding

National Natural Science Foundation of China 62071099
6 · The paper itself

Abstract

The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made

Indexed as

Computer SimulationHistocompatibility Antigens Class IIPeptidesAlgorithmsAnimalsHumansHistocompatibility Antigens Class IIPeptidesanti-drug antibodiesdeep learninghuman leukocyte antigen.immunogenicitymachine learningmajor histocompatibility complexTherapeutic peptides

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.