Method for predicting sensitivity of anticancer drugs based on deep factorization machine
By combining deep neural networks and factor decomposition machines to construct a deep factor decomposition machine model, the problem of inaccurate feature extraction in the existing technology for predicting anticancer drug sensitivity is solved, and more accurate and stable drug sensitivity analysis is achieved.
CN118919096BActive Publication Date: 2025-10-17THE ACAD OF TIANJIN UNIV HEFEI
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Patent Information
- Application Number
- CN202410924385.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-11
- Publication Date
- 2025-10-17
- Estimated Expiration
- 2044-07-11
AI Technical Summary
Technical Problem
When predicting anticancer drug sensitivity, existing technologies lack accuracy in feature extraction, and multivariate network models fail to fully consider relevant data connections, resulting in inaccurate drug sensitivity analysis.
Method used
Combining deep neural networks and factorization machines, a deep factorization machine model is constructed. By fusing low-order and high-order feature extraction, gene expression data and drug chemical structure data are used to predict anticancer drug sensitivity.
Benefits of technology
The accuracy and stability of drug sensitivity prediction are improved, and a more accurate multivariate network model is constructed, which can better capture the complex nonlinear relationship between drugs and genes.
✦ Generated by Eureka AI based on patent content.
Abstract
The application is based on a method for predicting anticancer drug sensitivity by deep factor decomposition machine, characterized in that it comprises the following steps: step 1, constructing a data set and preprocessing; step 2, constructing a deep factor decomposition machine DeepFM model: the main idea of the deep factor decomposition machine DeepFM model is to construct a factor decomposition machine FM model, construct a deep neural network model DNN, and fuse the factor decomposition machine FM and the deep neural network to construct a deep factor decomposition machine DeepFM model; step 3, training and evaluating the model results of each stage, and dividing the anticancer drug sensitivity data obtained in step 1 into 5 parts, performing 5-fold cross-validation, and training the model results of each stage in this stage. The application separately models the low-order feature combination of the drug, and fuses the high-order feature combination to construct a model that fuses the deep neural network model and the factor decomposition machine model, better extracts the drug feature structure, and makes the constructed model more accurate and higher in precision.
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