18264254. RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE simplified abstract (BOE Technology Group Co., Ltd.)
Contents
- 1 RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about RNA-protein interaction prediction
- 1.13 Original Abstract Submitted
RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE
Organization Name
BOE Technology Group Co., Ltd.
Inventor(s)
Zhenzhong Zhang of Beijing (CN)
RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18264254 titled 'RNA-PROTEIN INTERACTION PREDICTION METHOD AND APPARATUS, AND MEDIUM AND ELECTRONIC DEVICE
Simplified Explanation
This patent application describes a method for predicting RNA-protein interactions by encoding RNA and protein sequences into vector sequences and using a selective attention mechanism model to determine the probability of interaction between them.
- Obtaining RNA and protein sequences to be predicted
- Encoding RNA and protein sequences into vector sequences
- Using a selective attention mechanism model to obtain relevance vector sequences
- Determining the probability of interaction between the RNA and protein sequences
Key Features and Innovation
- Prediction of RNA-protein interactions
- Utilization of selective attention mechanism model
- Encoding of RNA and protein sequences into vector sequences
Potential Applications
This technology can be used in bioinformatics, drug discovery, and understanding gene regulation mechanisms.
Problems Solved
- Predicting RNA-protein interactions accurately
- Enhancing understanding of molecular interactions
Benefits
- Improved accuracy in predicting RNA-protein interactions
- Facilitates research in molecular biology and drug development
Commercial Applications
- Bioinformatics software development
- Pharmaceutical research and development
Prior Art
No information provided.
Frequently Updated Research
No information provided.
Questions about RNA-protein interaction prediction
Question 1
How does the selective attention mechanism model improve the accuracy of predicting RNA-protein interactions?
The selective attention mechanism model focuses on relevant parts of the input sequences, allowing for a more precise determination of the interaction probability.
Question 2
What are the potential implications of inaccurate predictions of RNA-protein interactions in drug discovery?
Inaccurate predictions could lead to the development of ineffective drugs or misinterpretation of biological processes, potentially hindering drug discovery efforts.
Original Abstract Submitted
A method for RNA-protein interaction prediction, including: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.
