Single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning

Abstract

The invention provides a single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning, and aims to solve the problem that the accuracy of a traditional drug sensitivity prediction method is limited. According to the system, multi-modal bioinformatics data including cell line gene expression data, proteome data, metabolome data, drug reaction data and single-cell RNA sequencing data are integrated, and feature extraction, fusion and selection are performed by using a deep reinforcement learning technology, so that high-precision prediction of single-cell drug sensitivity is realized. The algorithm mainly comprises five core modules: a multi-modal data integration and preprocessing module, a deep reinforcement learning feature extraction and fusion module, a dynamic feature selection and importance evaluation module, a cross-domain migration and adaptive learning module and a deep reinforcement learning prediction model and performance optimization module. The modules work cooperatively, potential features can be learned automatically from multi-modal data, a feature fusion strategy can be optimized, a key feature subset can be selected dynamically, and a model structure and parameters can be adjusted adaptively according to features of target domain data, so that prediction accuracy and stability are improved remarkably. Experimental results show that the algorithm shows significant advantages in multiple evaluation indexes, and compared with a traditional prediction method, the algorithm has higher prediction accuracy and wider applicability. Therefore, the invention provides a novel and efficient drug sensitivity prediction tool for the fields of personalized medical treatment and drug research and development, and has important practical application value.

Description

Technical field:

[0001] The present invention relates to the fields of bioinformatics, machine learning and drug research and development, and specifically is an algorithm that utilizes deep reinforcement learning technology, combined with multimodal data fusion and dynamic feature extraction and transfer learning strategies, to achieve high-precision prediction of single-cell drug sensitivity. Background technology:

[0002] Single-cell drug sensitivity prediction is a key step in drug development and personalized treatment. However, traditional methods usually rely on single gene expression data and ignore other biomolecular information, such as proteome and metabolome data, resulting in limited prediction accuracy. In addition, the heterogeneity between different individuals or cell lines also makes drug sensitivity prediction more complicated. Therefore, it is of great significance to develop a system that can integrate multimodal data, capture dynamic features, and accurately predict single-cell drug sensitivity. Summary of the invention:

[0003] To solve the above problems, the present invention proposes a single-cell drug sensitivity prediction algorithm based on multimodal reinforcement learning. The algorithm integrates multimodal data from different sources, uses deep reinforcement learning for feature extraction and fusion, dynamic feature selection and importance evaluation, cross-domain migration and adaptive learning, and constructs a deep reinforcement learning prediction model, achieving high-precision prediction of single-cell drug sensitivity.

[0004] Technical solution:

[0005] The system architecture of the present invention mainly includes the following five core modules:

[0006] Multimodal data integration and preprocessing module: responsible for collecting cell line gene expression data, proteomic data, metabolomic data, drug response data and single-cell RNA sequencing data from multiple bioinformatics databases and laboratory data, and performing data cleaning, normalization and enhancement processing to ensure the integrity and accuracy of the data.

[0007] Deep reinforcement learning feature extraction and fusion module: Design a feature extractor based on deep convolutional neural network (CNN) or long short-term memory network (LSTM), automatically learn potential features from multimodal data, and introduce reinforcement learning mechanism to optimize the fusion strategy of features from different sources through reward function to achieve effective integration of features.

[0008] Dynamic feature selection and importance assessment module: Based on the reinforcement learning strategy, dynamically select the most critical feature subset for drug response prediction, and use the value function or policy gradient in the reinforcement learning process to evaluate the contribution of each feature to the prediction result.

[0009] Cross-domain transfer and adaptive learning module: Utilize the information of the source domain to improve the performance of the target domain through transfer learning, and adaptively adjust the model structure and parameters according to the characteristics of the target domain data to improve the generalization ability of the prediction.

[0010] Deep reinforcement learning prediction model and performance optimization module: Build a prediction model based on the deep reinforcement learning framework, combine the results of feature extraction, fusion and selection to predict drug response, and improve the prediction accuracy and stability of the model through policy gradient optimization, hyperparameter tuning and integrated learning methods.

[0011] Beneficial effects:

[0012] 1. Using deep reinforcement learning for feature extraction, fusion and selection effectively improves the accuracy and stability of drug sensitivity prediction.

[0013] 2. Integrates multimodal data and makes full use of information at the biomolecular level.

[0014] 3. It captures the dynamic characteristics of the data and adapts to the heterogeneity between different individuals or cell lines. Description of the drawings:

[0015] Figure 1 This is the algorithm system architecture diagram;

[0016] Figure 2 This is a graph showing comparative experimental results. Specific implementation method:

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] Reference Figure 1 , a single-cell drug sensitivity prediction algorithm based on multimodal reinforcement learning, including the following modules and steps:

[0019] 1. System architecture and module functions

[0020] The system architecture of the present invention mainly includes five core modules, each of which has specific functions and tasks, and together constitutes a complete single-cell drug sensitivity prediction system.

[0021] 1.1 Multimodal Data Integration and Preprocessing Module

[0022] Data Collection: Cell line gene expression data, proteomic data, metabolomic data, drug response data, and single-cell RNA sequencing data were collected from multiple bioinformatics databases and laboratory data.

[0023] Data cleaning: remove noise and outliers from data, fill in missing values, and ensure data integrity and accuracy.

[0024] Data normalization: Normalize data from different sources to make them numerically comparable for subsequent analysis.

[0025] Data enhancement: Use data augmentation, data transformation and other technologies to increase the diversity and robustness of data and improve the generalization ability of the model.

[0026] 1.2 Deep reinforcement learning feature extraction and fusion module

[0027] Deep Feature Extraction Network: Design a feature extractor based on a deep convolutional neural network (CNN) or a long short-term memory network (LSTM) to automatically learn latent features from multimodal data.

[0028] Reinforcement learning fusion strategy: Introduce reinforcement learning mechanism, optimize the fusion strategy of features from different sources through reward function, and realize effective integration of features.

[0029] 1.3 Dynamic feature selection and importance assessment module

[0030] Dynamic feature selection algorithm: A reinforcement learning-based strategy that dynamically selects the most critical feature subsets for drug response prediction.

[0031] Feature importance evaluation: Use the value function or policy gradient in the reinforcement learning process to evaluate the contribution of each feature to the prediction result.

[0032] 1.4 Cross-domain migration and adaptive learning module

[0033] Cross-domain transfer learning: Using information from the source domain (e.g., cell lines with rich known drug response data) to improve the performance of the target domain (e.g., response prediction of new drugs or new cell lines) through transfer learning.

[0034] Adaptive learning mechanism: Adaptively adjust the model structure and parameters according to the characteristics of the target domain data to improve the generalization ability of the prediction.

[0035] 1.5 Deep reinforcement learning prediction model and performance optimization module

[0036] Prediction model construction: Based on the deep reinforcement learning framework, a prediction model is constructed to predict drug response by combining the results of feature extraction, fusion and selection.

[0037] Performance optimization: Use policy gradient optimization, hyperparameter tuning, and ensemble learning methods in reinforcement learning to improve the prediction accuracy and stability of the model.

[0038] 2. Experiment and verification

[0039] In order to verify the effectiveness of the present invention, we conducted the following experiments:

[0040] 2.1 Dataset Preparation

[0041] Dataset source: Single-cell RNA sequencing data, gene expression data, proteomic data, metabolomic data, and drug response data were collected from multiple bioinformatics public databases and laboratory data.

[0042] Dataset division: The dataset is divided into training set, validation set and test set. The training set is used to train the model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the model performance.

[0043] 2.2 Model training and validation

[0044] Model training: Use training set data to train deep learning prediction models, including training of modules such as dynamic feature learning networks, multimodal fusion mechanisms, and adaptive feature selection.

[0045] Model validation: Adjust model parameters through validation set data, including hyperparameter tuning, regularization strength, etc., to improve the accuracy and robustness of the model.

[0046] Model evaluation: Use test set data to evaluate model performance. Evaluation indicators include accuracy, recall, F1 score, AUC, etc., and compare with traditional prediction methods.

[0047] 2.3 Results Analysis

[0048] Performance comparison: The system of the present invention is compared with traditional methods, including prediction methods based on single modal data, prediction methods based on static feature extraction, etc. By comparing the experimental results, Figure 2 As shown, the system of the present invention shows significant advantages in multiple evaluation indicators.

[0049] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A single-cell drug sensitivity prediction algorithm based on multimodal reinforcement learning, characterized by: It includes the following steps and components: 1) Multimodal data integration and preprocessing module: Integrate multimodal data from different sources, including but not limited to cell line gene expression data, proteome data, metabolome data, drug response data, and single-cell RNA sequencing data. Use advanced data cleaning and normalization techniques to ensure data quality, and use data enhancement strategies to increase data diversity. 2) Deep reinforcement learning feature extraction and fusion module. This module is designed with a feature extractor based on deep convolutional neural network (CNN) or long short-term memory network (LSTM), which can automatically learn potential features from multimodal data. At the same time, a reinforcement learning mechanism is introduced to optimize the fusion strategy of features from different sources through the reward function to achieve effective integration of features. 3) Dynamic feature selection and importance assessment module, which dynamically selects the most critical feature subset for drug response prediction based on reinforcement learning strategy, and uses the value function or policy gradient in the reinforcement learning process to evaluate the contribution of each feature to the prediction result. 4) The cross-domain transfer and adaptive learning module uses the information of the source domain (such as cell lines with rich known drug response data) to improve the performance of the target domain (such as response prediction of new drugs or new cell lines) through transfer learning. At the same time, it adaptively adjusts the model structure and parameters according to the characteristics of the target domain data to improve the generalization ability of the prediction. 5) Deep reinforcement learning prediction model and performance optimization module: build a prediction model based on the deep reinforcement learning framework, combine the results of feature extraction, fusion and selection to predict drug response, and improve the prediction accuracy and stability of the model through strategy gradient optimization, hyperparameter tuning and integrated learning methods.

2. The system according to claim 1, characterized in that: The multimodal data integration and preprocessing module further includes: a data cleaning step for removing noise and outliers in the data; a data filling step for filling missing values; a data normalization step for normalizing data from different sources to make them numerically comparable; a data enhancement step for increasing the diversity and robustness of the data using data amplification, data transformation and other techniques.

3. The system according to claim 1, characterized in that In the deep reinforcement learning feature extraction and fusion module, the reinforcement learning fusion strategy optimizes the feature fusion strategy by designing a specific reward function to reward or punish according to the contribution of the fused features to the prediction results.

4. The system according to claim 1, characterized in that In the dynamic feature selection and importance assessment module, feature importance assessment utilizes the value function or policy gradient in the reinforcement learning process to dynamically assess the weight and contribution of each feature in the prediction process to guide the feature selection process.

5. The system according to claim 1, characterized in that In the cross-domain migration and adaptive learning module, the adaptive learning mechanism adaptively adjusts the parameters such as the number of layers, number of nodes, and learning rate of the deep learning model according to the characteristics of the target domain data to improve the prediction performance of the model in the target domain.

6. The system according to claim 1, characterized in that In the deep reinforcement learning prediction model and performance optimization module, a policy gradient optimization algorithm is used to train the prediction model, and hyperparameter tuning and ensemble learning methods are combined to further improve the prediction accuracy and stability of the model.

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