Multi-task Immunogenicity Prediction Model Training
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Solution Overview
Problem
Current immunogenicity prediction models for epitopes face challenges due to a small quantity of pMHCs with immunogenicity labels, leading to poor learning effects and low accuracy in predicting immunogenicity.
Innovation Solution
The development of an immunogenicity prediction model training method that includes a binding prediction sub-model, a presentation prediction sub-model, and an immunogenicity prediction sub-model, which are trained together using multi-task learning to overcome the limitations of limited labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-task learning methods are used for immunogenicity prediction, then the model structure is simple and easy to implement, but the prediction accuracy is low due to limited labeled data
Solution Approach 1:
The patent combines three separate prediction tasks (binding affinity prediction, presentation prediction, and immunogenicity prediction) into a single unified multi-task learning model. The model shares common layers for feature extraction while having separate output heads for each task, allowing the tasks to learn complementary features that improve overall prediction accuracy despite limited labeled data
Solution Approach 2:
The model is designed to perform multiple functions simultaneously - predicting binding affinity, presentation levels, and immunogenicity outcomes. By making the model universal across these related tasks, it can leverage data from tasks with more abundant labels (like binding affinity) to improve performance on the target task (immunogenicity prediction) even when labeled data is scarce
2Measurement precision
If multi-task learning is applied to improve prediction accuracy, then the prediction accuracy improves to AUC of 0.9, but the model complexity increases with multiple sub-models
Solution Approach 1:
The multi-task learning model is segmented into distinct functional modules: a shared feature extraction backbone and separate task-specific output heads. This segmentation allows each sub-model to focus on its specific prediction task while sharing common learned representations, making the overall complex model more manageable and interpretable
Solution Approach 2:
The model structure implements a nested architecture where the shared feature extraction layers are nested within the overall model framework, and task-specific layers are nested on top of the shared features. This nested structure allows efficient parameter sharing while maintaining task-specific capabilities, balancing model complexity with performance
Data Source
AI summary
An immunogenicity prediction model training method includes constructing an immunogenicity prediction model that includes a binding prediction sub-model, a presentation prediction sub-model, and an immunogenicity prediction sub-model, the binding prediction sub-model being configured to predict binding between an epitope and a major histocompatibility complex (MHC), the presentation prediction sub-model being configured to predict cell membrane presentation of an antigenic peptide-major histocompatibility complex (pMHC) molecular complex, and the immunogenicity prediction sub-model being configured to predict immunogenicity of the pMHC; inputting a first sample pair into the immunogenicity prediction model to obtain a sample immunogenicity prediction result, the first sample pair including a first sample epitope sequence and a first sample MHC sequence, and the first sample pair being provided with a first sample immunogenicity label; and training the immunogenicity prediction model with the first sample immunogenicity label as a supervisor of the sample immunogenicity prediction result.