A protein sequence fluorescence intensity prediction method, system, device and storage medium
By using generative adversarial networks and feature extraction techniques, realistic training data is generated and accurate features are extracted, which solves the problem of insufficient data in protein fluorescence intensity prediction models and improves prediction accuracy.
CN121983141BActive Publication Date: 2026-06-09SOUTH CHINA UNIV OF TECH
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTH CHINA UNIV OF TECH
- Filing Date
- 2026-04-07
- Publication Date
- 2026-06-09
AI Technical Summary
Technical Problem
Existing protein fluorescence intensity prediction models suffer from low accuracy due to slow data annotation and limited data volume.
Method used
Generative adversarial networks are used to generate a large amount of realistic training data. Through mutual learning between the generator and the discriminator, combined with nonlinear projection and local pattern enhancement techniques, features of protein sequences are extracted to form the final features for prediction.
Benefits of technology
It improves the accuracy of protein sequence fluorescence intensity prediction, generates realistic data and accurately extracts features, overcomes the problem of information mixing and dilution in traditional methods, and improves the accuracy of prediction models.
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Figure CN121983141B_ABST
Abstract
This invention belongs to the field of protein fluorescence intensity prediction, specifically relating to a method, system, device, and storage medium for predicting the fluorescence intensity of protein sequences. The method includes: extracting features from a training dataset, training a generator and discriminator by incorporating random noise to obtain a generative adversarial model (GAP); supplementing data using the GAP to obtain a prediction dataset; extracting semantic features from the prediction dataset and performing local pattern enhancement on the hidden layer features; fusing these features to obtain the final features; using the final features as input to a prediction network to train the prediction network to obtain an optimal prediction network; and inputting the actual protein sequence into the optimal prediction network to obtain the fluorescence intensity corresponding to the actual protein sequence. This application improves the accuracy of predicting the fluorescence intensity of protein sequences.
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