A protein salt sensitivity prediction method based on feature engineering and ensemble learning
By employing feature engineering and ensemble learning methods, 10 physicochemical features of protein sequences were extracted, and a multi-model framework was constructed. This solved the problems of insufficient feature representation and model bias in liquid-liquid phase separation, and achieved high-precision salt concentration sensitivity prediction and enhanced interpretability.
CN121075496BActive Publication Date: 2026-03-27LUDONG UNIVERSITY
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Patent Information
- Application Number
- CN202511603878.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-05
- Publication Date
- 2026-03-27
- Estimated Expiration
- 2045-11-05
AI Technical Summary
Technical Problem
Existing technologies suffer from insufficient characterization, large model bias, and lack of interpretability in liquid-liquid phase separation, making it difficult to achieve high-precision salt concentration sensitivity prediction.
Method used
We employ a feature engineering and ensemble learning approach to extract 10 key physicochemical features from protein sequences. We then construct random forest, gradient boosting tree, and support vector machine models, and integrate these models through weighted voting to output the feature contribution.
Benefits of technology
It improves the accuracy and stability of salt concentration sensitivity prediction, reduces computational resource requirements, and enhances the interpretability of the model.
✦ Generated by Eureka AI based on patent content.
Abstract
The application belongs to the field of bioinformatics, and relates to a protein salt sensitivity prediction method based on feature engineering and ensemble learning. First, by inputting known low, medium and high salt concentration protein sequence data and performing data cleaning and length standardization, the system automatically extracts ten key physical and chemical characteristics such as net charge density and charge asymmetry. Second, a multi-model learning framework is built using random forest, gradient boosting tree and support vector machine, the integrated weight is dynamically allocated based on the accuracy of each model on the validation set, and the final classification result is generated by using the weighted voting mechanism. Finally, the integrated model is quantitatively evaluated and saved, and then new protein sequences are predicted, and the prediction category, confidence and feature importance analysis are output. The application realizes high-precision and interpretable prediction of protein salt sensitivity, and provides condition optimization support for liquid-liquid phase separation research and drug delivery system.
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