Sequence Transform Neural Network for MHC-Peptide Binding Prediction
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Solution Overview
Problem
Existing methods for predicting the binding of T cells to MHC-peptide complexes face challenges due to noise, high costs, and data imbalance, making it difficult to utilize all experimental data effectively.
Innovation Solution
The proposed solution involves categorizing various experimental methods into a limited number of categories and using these categorized methods as input features in an embedded vector for an artificial intelligence model. Additionally, data augmentation is performed by leveraging causal relationships between experimental results to improve data balance and prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all experimental methods are used as input features, then prediction accuracy may improve, but data imbalance and processing complexity increase
Solution Approach 1:
The patent segments experimental methods into multiple categories (e.g., binding assays, functional assays, structural analyses) and processes each category separately through category-specific embedding vectors. This segmentation allows the system to handle diverse experimental data without being overwhelmed by the sheer volume and variety of individual experimental methods, thereby reducing processing complexity while maintaining comprehensive predictive capability.
Solution Approach 2:
The patent creates a universal framework where category embeddings serve as multi-functional input features that can be combined with other data types (sequence information, structural data) for prediction. The category embedding layer acts as a universal interface that accommodates different experimental methods within their respective categories, enabling the system to leverage all experimental data while maintaining manageable complexity through standardized processing protocols.
2Measurement precision
If experimental data is used to improve binding prediction, then prediction accuracy improves, but data imbalance occurs due to varying amounts of data per experimental method
Solution Approach 1:
The patent merges data from multiple experimental methods within the same category into a unified category embedding vector. By aggregating information across different experimental methods (e.g., combining results from multiple binding assays), the system creates balanced representation for each category, mitigating the impact of data imbalance at the individual experimental method level while maintaining comprehensive predictive power.
Solution Approach 2:
The patent transforms raw experimental data into normalized embedding vectors that represent categories in a standardized parameter space. This parameter transformation rescales and normalizes data from different experimental methods, creating balanced representations that can be uniformly processed by the prediction model regardless of the original data volume or distribution of individual experimental methods.
3Adaptability or versatility
If multiple experimental methods are combined, then comprehensive prediction is achieved, but the range of input features becomes too large to process effectively
Solution Approach 1:
The patent extracts the essential characteristics of experimental methods into category-level embedding vectors, separating the critical predictive information from the redundant detail of individual experimental methods. By taking out only the necessary categorical information (e.g., assay type, measurement parameter) and discarding unnecessary method-specific noise, the system maintains comprehensive prediction capability while dramatically reducing input feature dimensionality.
Solution Approach 2:
The patent transforms high-dimensional raw experimental data into lower-dimensional category embedding vectors through dimensionality reduction techniques. By projecting multiple experimental methods into a condensed embedding space that captures essential categorical characteristics, the system achieves comprehensive prediction capability while operating with manageable input feature dimensions that can be effectively processed by neural networks.
Data Source
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AI summary
An apparatus for implementing a sequence transform neural network for transforming an input sequence having network inputs comprises a memory; a communicator; and a processor operably connected to the memory and the communicator The processor is configured to: receive first input data, receive second input data corresponding to the first input data, and generate output data predicting whether the first input data and the second input data are combined by further incorporating information corresponding to an experimental method into an embedding vector, determined by performing predetermined attention computation based the first input data and the second input data labeled on predetermined label information. The experimental method is comprised in one of a plurality of predetermined categories, and the experimental method and the experimental result data of the experimental method are grouped according to the predetermined categories and combined with the embedding vector to be used as an input feature.