Vector Model Training for RNA-Protein Negative Sample Similarity
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Solution Overview
Problem
Existing methods for generating negative samples for RNA-protein interaction prediction models result in low-quality samples, leading to poor performance due to insufficient similarity to positive samples, which affects the accuracy of interaction prediction.
Innovation Solution
A method for training a vector model that utilizes RNA and protein sequences to generate high-quality negative samples by calculating similarities and interactions, using a recurrent neural network to vectorize k-mer subsequences and iteratively updating model parameters based on an objective function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing methods are used to generate negative samples, then the generation process is simple, but the quality of negative samples is low and similarity to positive samples is insufficient
Solution Approach 1:
The patent introduces a vector model as an intermediary tool to generate negative samples. The vector model transforms RNA and protein sequences into vector representations, enabling sophisticated similarity calculations and interaction predictions. This intermediary mechanism allows the system to generate high-quality negative samples with controlled similarity to positive samples, resolving the contradiction between sample quality and method complexity.
Solution Approach 2:
The patent changes the parameter space by transforming sequences into vector representations and using continuous vector operations instead of discrete sequence manipulations. By adjusting similarity thresholds and using vector arithmetic operations, the system can precisely control the characteristics of generated negative samples, improving quality while managing complexity through parameterized control.
2Reliability
If simple methods are used to generate negative samples, then the process is efficient, but the prediction accuracy of interaction models deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-training the vector model on large datasets of RNA-protein interactions before using it to generate negative samples. This pre-training phase establishes the vector model's ability to capture meaningful biological relationships, ensuring that subsequently generated negative samples are of high quality. The preliminary action separates the computationally intensive model training from the sample generation process, improving overall efficiency.
Solution Approach 2:
The patent uses the vector model to create copies of positive samples with modified characteristics. By transforming sequences into vectors, manipulating them through vector operations, and transforming back, the system generates negative samples that preserve structural and functional properties while altering interaction outcomes. This copying mechanism ensures high-quality samples that maintain biological plausibility.
3Manufacturing precision
If high-quality negative samples are generated using vector models, then the similarity to positive samples is improved, but the computational complexity increases
Solution Approach 1:
The patent replaces traditional mechanical sequence manipulation methods with vector-based computational operations. Instead of directly manipulating RNA and protein sequences through complex algorithms, the system transforms them into vector representations and uses efficient vector arithmetic operations. This substitution reduces computational complexity while maintaining or improving the precision of similarity control in generated negative samples.
Data Source
AI summary
A method for training a vector model, including: obtaining more than one RNA sequence and more than one protein sequence; obtaining more than one first RNA vector by vectorizing the more than one RNA sequence; obtaining more than one first protein vector by vectorizing the more than one protein sequence; determining an interaction between the RNA sequence and the protein sequence according to the first RNA vector and the first protein vector; obtaining a similarity of more than one RNA-RNA pair by calculating a distance between any two RNA sequences; obtaining a similarity of more than one protein-protein pair by calculating a distance between any two protein sequences; training the vector model according to an interaction between the RNA sequence and the protein sequence, the similarity of the RNA-RNA pair and the similarity of the protein-protein pair.