NeoGuider: neoepitope prediction using advanced feature engineering.

Xiaofei Zhao, Lei Wei, Zhen Xie, Xuegong Zhang

Abstract read

In one paragraph

Article in Genome medicine, 2025. 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.

Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026
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

4 authors.

Xiaofei ZhaoMOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST, Center for Synthetic and Systems Biology and Department of Automation, Tsinghua University, Beijing, China.

Lei WeiMOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST, Center for Synthetic and Systems Biology and Department of Automation, Tsinghua University, Beijing, China. weilei92@tsinghua.edu.cn.

Zhen XieMOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST, Center for Synthetic and Systems Biology and Department of Automation, Tsinghua University, Beijing, China.

Xuegong ZhangMOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST, Center for Synthetic and Systems Biology and Department of Automation, Tsinghua University, Beijing, China. zhangxg@tsinghua.edu.cn.

Funding

Chinese Government Scholarship 2022GXZ005806National Natural Science Foundation of China 62373210National Natural Science Foundation of China 92470105Tsinghua-Toyota Joint Research Institute Inter-Disciplinary Program 20243930093

6 · The paper itself

Abstract

The design of neoepitope-based cancer immunotherapy requires predicting the immunogenicity of each patient-specific neoepitope candidate. We designed NeoGuider, a machine-learning model to address nonlinearity and class imbalance in such prediction. Central to NeoGuider is a supervised feature-transformation approach, which estimates odds using our custom kernel density estimation followed by centered isotonic regression. We implemented NeoGuider as a bioinformatics pipeline, which detects neoepitope candidates from sequencing data and prioritizes such candidates by predicted immunogenicity. Benchmarking on 7 cohorts, 113 patients, and 635 immunogenic candidates showed that NeoGuider outperformed existing methods in neoepitope prediction. NeoGuider is open-source at https://github.com/XuegongLab/neoguider .

Indexed as

Computational BiologyEpitopesNeoplasmsSoftwareHumansImmunotherapyMachine LearningEpitopesCancer immunotherapyClass imbalanceFeature engineeringNeoantigenNeoepitopeNext-generation sequencingNonlinearity

Identifiers

PMID41437280

DOI10.1186/s13073-025-01592-9

PMCPMC12838086

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.