Boe technology group co., ltd. (20240265993). METHOD FOR TRAINING VECTOR MODEL AND GENERATING NEGATIVE SAMPLE simplified abstract

METHOD FOR TRAINING VECTOR MODEL AND GENERATING NEGATIVE SAMPLE

Organization Name

boe technology group co., ltd.

Inventor(s)

Zhenzhong Zhang of Beijing (CN)

METHOD FOR TRAINING VECTOR MODEL AND GENERATING NEGATIVE SAMPLE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240265993 titled 'METHOD FOR TRAINING VECTOR MODEL AND GENERATING NEGATIVE SAMPLE

• • Simplified Explanation:**

This patent application describes a method for training a vector model using RNA and protein sequences to determine interactions and similarities between them.

• • Key Features and Innovation:**
• Obtaining multiple RNA and protein sequences.
• Vectorizing the sequences to obtain RNA and protein vectors.
• Determining interactions between RNA and protein sequences.
• Calculating similarities between RNA-RNA and protein-protein pairs.
• Training the vector model based on interactions and similarities.

• • Potential Applications:**

This technology can be applied in bioinformatics, drug discovery, and protein engineering.

• • Problems Solved:**

The technology helps in understanding the interactions between RNA and protein sequences, which is crucial in various biological processes.

• • Benefits:**
• Improved understanding of RNA-protein interactions.
• Enhanced accuracy in predicting similarities between sequences.
• Facilitates the development of new drugs and therapies.

• • Commercial Applications:**

Potential commercial applications include pharmaceutical research, personalized medicine, and biotechnology industries.

• • Prior Art:**

Researchers can explore existing literature on RNA-protein interactions and sequence analysis methods in bioinformatics.

• • Frequently Updated Research:**

Stay updated on the latest advancements in RNA-protein interaction studies and vector model training techniques.

• • Questions about RNA-Protein Interaction:**

1. How does this technology contribute to the field of personalized medicine? 2. What are the implications of accurately predicting RNA-protein interactions in drug discovery?

1. **A relevant generic question not answered by the article, with a detailed answer:** How does this technology compare to existing methods for analyzing RNA-protein interactions? This technology offers a comprehensive approach by considering both interactions and similarities between sequences, providing a more holistic understanding of biological processes.

2. **Another relevant generic question, with a detailed answer:** What are the potential challenges in implementing this technology in drug development? One challenge could be the need for large datasets to train the vector model effectively, which may require collaboration with research institutions or access to extensive databases.

Original Abstract Submitted

a method for training a vector model, including: obtaining more than one rna sequence and more than one protein sequence; obtaining more than one first rna vector by vectorizing the more than one rna sequence; obtaining more than one first protein vector by vectorizing the more than one protein sequence; determining an interaction between the rna sequence and the protein sequence according to the first rna vector and the first protein vector; obtaining a similarity of more than one rna-rna pair by calculating a distance between any two rna sequences; obtaining a similarity of more than one protein-protein pair by calculating a distance between any two protein sequences; training the vector model according to an interaction between the rna sequence and the protein sequence, the similarity of the rna-rna pair and the similarity of the protein-protein pair.