Sequence Transduction Neural Network for Neoantigen Prediction
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Solution Overview
Problem
Existing AI-based neural networks face challenges in deriving accurate results for data prediction, particularly in the context of neoantigen prediction for immune anticancer vaccine treatment.
Innovation Solution
A sequence transduction neural network is implemented, trained using an attention operation with labeled data, and configured to receive input sequences for predicting neoantigen candidates, thereby enhancing the accuracy of neoantigen prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks are used for data prediction, then the system is simpler to implement, but the prediction accuracy is insufficient
Solution Approach 1:
The patent segments the prediction task into multiple stages: embedding layer for feature representation, encoder layer with self-attention for sequence understanding, decoder layer with cross-attention for prediction, and output layer for result generation. This segmentation allows each component to specialize in specific aspects of the prediction task, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate representations including embedding vectors that transform input data into meaningful feature spaces, and attention mechanisms that serve as intermediaries to selectively focus on relevant input sequences. These intermediaries enable the system to capture complex patterns and relationships, significantly improving prediction accuracy without requiring direct complex connections between all input and output elements.
2Reliability
If more complex training algorithms are developed to improve prediction accuracy, then the prediction probability increases, but the training complexity and computational resources required increase
Solution Approach 1:
The patent employs preliminary action through pre-training strategies where the embedding layer and encoder are trained on large corpora of sequence data before fine-tuning on the specific prediction task. This pre-training establishes robust feature representations and sequence understanding, allowing the model to achieve high prediction probability with relatively simple task-specific training algorithms and reduced computational requirements.
Solution Approach 2:
The patent implements feedback mechanisms through loss functions that compare predicted outputs with ground truth labels, generating gradient signals that guide parameter updates. The attention mechanisms also provide feedback by dynamically adjusting weights based on input sequence characteristics, enabling the model to learn from prediction errors and improve reliability through iterative refinement without requiring overly complex training algorithms.
Data Source
AI summary
A neural network implementation device may comprise at least one memory and at least one processor. The at least one processor is configured to: receive first input data; receive second input data corresponding to the first input data; train a sequence transduction neural network by performing an attention operation using the first input data and the second input data labeled with predetermined label information; and determine output data output by the sequence transduction neural network trained using the first input data, the second input data, and the label information.