ArticleInternational journal of molecular sciences2022
Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Personalized neoantigen mRNA vaccines for pancreatic cancer: the role of lipid nanoparticle delivery systems.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Review
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Epitope Generation Gateway: A biological context-dependent approach to cancer vaccine development.GigaScience · 2026Article
- Beyond counting: how single-cell long-read sequencing turns transcriptome complexity into precision targets.Frontiers in oncology · 2026Review
- Computational Methods for Cancer Neoantigen Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- CNNeoPP: a large language model-enhanced deep learning pipeline for personalized neoantigen prediction and liquid biopsy applications.Frontiers in immunology · 2026Article
- Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources.Genomics, proteomics & bioinformatics · 2025Review
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey.Briefings in bioinformatics · 2025Review
- Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer.Frontiers in immunology · 2025Article
- Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design.Human vaccines & immunotherapeutics · 2024Article
- Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.Biology direct · 2024Article
- Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies.Vaccines · 2024Article
- Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens.Vaccines · 2024Review
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
- Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines.The Journal of clinical investigation · 2024Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.
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Registered trials
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.