Evidence map›Paper›PMID 36698417›Full record

ReviewFrontiers in oncology2022

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy.

Yu Cai, Rui Chen, Shenghan Gao, Wenqing Li, Yuru Liu, Guodong Su, Mingming Song, Mengju Jiang, Chao Jiang, Xi Zhang

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
10.3field-weighted citation impact, top 1% of its field
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

46 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.

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  5. Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026
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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

10 authors at 2 institutions in 1 country.

Yu CaiSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Rui ChenSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Shenghan GaoSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Wenqing LiSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Yuru LiuSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Guodong SuSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Mingming SongSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Mengju JiangSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Chao JiangDepartment of Neurology, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, Shaanxi, China.
Xi ZhangSchool of Medicine, Northwest University, Xi'an, Shaanxi, China.
Northwest University · CNXi'an Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Indexed as

artificial intelligencecancer immunotherapycancer neoantigenneoantigen predictionnext generation sequencing

Identifiers

PMID36698417
PMCPMC9868469
OpenAlexW4313827627

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.