Source 1primary paper2026MED
Toolkit/CNNeoPP
CNNeoPP
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Here, we present CNNeoPP, an integrated computational pipeline for neoantigen discovery. CNNeoPP was rigorously validated using independent datasets, including the TESLA dataset, and experimental validation via ELISpot T-cell assays.
Usefulness & Problems
No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.
Published Workflows
Objective: Develop and validate an integrated computational pipeline for personalized neoantigen discovery, prioritization, and exploratory liquid biopsy application.
Why it works: The abstract states that CNNeo uses large language model-derived sequence representations and multi-modal feature integration, and that CNNeoPP combines computational prioritization with validation on independent datasets and ELISpot assays.
large language model-derived sequence representationmulti-modal feature integrationdeep learningindependent dataset validationELISpot T-cell assay validationplasma cell-free DNA proof-of-concept analysis
Stages
- 1.Deep learning neoantigen prediction(broad_screen)
This stage generates neoantigen predictions to address limitations in existing predictive models.
Selection: Prediction of immunogenic neoantigens using large language model-derived sequence representations and multi-modal feature integration.
- 2.Independent dataset validation(confirmatory_validation)
This stage tests whether the pipeline's predictive performance holds on independent benchmark data.
Selection: Validation on independent datasets including TESLA.
- 3.Experimental ELISpot validation(confirmatory_validation)
This stage provides experimental confirmation beyond computational prediction.
Selection: Experimental validation via ELISpot T-cell assays.
- 4.Plasma cell-free DNA proof-of-concept application(secondary_characterization)
This stage explores whether the pipeline can support non-invasive neoantigen prediction from plasma cell-free DNA.
Selection: Use of plasma cell-free DNA to explore feasibility of non-invasive neoantigen prediction and assess sequencing depth effects on detectability.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Target processes
recombinationValidation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
CNNeoPP was used in a proof-of-concept study with plasma cell-free DNA to explore non-invasive neoantigen prediction.
CNNeo demonstrates superior predictive performance compared with existing tools for neoantigen prediction.
CNNeoDB is a publicly accessible database compiling neoantigen data from multiple sources.
Increased sequencing depth enhances neoantigen detectability, and this effect is further amplified by the prioritization strategy of CNNeoPP.
CNNeoPP was validated using independent datasets including TESLA and by experimental ELISpot T-cell assays.
Approval Evidence
1 source3 linked approval claimsfirst-pass slug cnneopp
Here, we present CNNeoPP, an integrated computational pipeline for neoantigen discovery. CNNeoPP was rigorously validated using independent datasets, including the TESLA dataset, and experimental validation via ELISpot T-cell assays.
Source:
applicationsupports
CNNeoPP was used in a proof-of-concept study with plasma cell-free DNA to explore non-invasive neoantigen prediction.
Source:
sequencing depth effectsupports
Increased sequencing depth enhances neoantigen detectability, and this effect is further amplified by the prioritization strategy of CNNeoPP.
Source:
validationsupports
CNNeoPP was validated using independent datasets including TESLA and by experimental ELISpot T-cell assays.
Source:
Comparisons
No literature-backed comparison notes have been materialized for this record yet.
Ranked Citations
- 1.