18018858. METHOD FOR TRAINING VECTOR MODEL AND GENERATING NEGATIVE SAMPLE simplified abstract (BOE Technology Group Co., Ltd.)

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 18018858 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 create RNA and protein vectors.
• Determining interactions between RNA and protein sequences.
• Calculating distances to obtain similarities between RNA-RNA and protein-protein pairs.
• Training the vector model based on interactions and similarities.

• • Potential Applications:**

This technology could be used in bioinformatics, drug discovery, and protein engineering to analyze interactions between RNA and proteins.

• • Problems Solved:**

This method addresses the challenge of efficiently analyzing and predicting interactions between RNA and protein sequences.

• • Benefits:**
• Improved understanding of RNA-protein interactions.
• Enhanced accuracy in predicting similarities between RNA and protein pairs.
• Potential for advancements in drug development and personalized medicine.

• • Commercial Applications:**

The technology could be valuable in pharmaceutical research, biotechnology companies, and academic research institutions for studying molecular interactions.

• • Questions about RNA-Protein Interaction:**

1. How does this method improve the analysis of RNA-protein interactions? 2. What are the potential implications of accurately predicting similarities between RNA and protein sequences?

• • Frequently Updated Research:**

Researchers are continually exploring new applications and refinements of this technology in the fields of genomics and proteomics.

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.