Biological Data Graph Querying With Neural Embeddings
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Solution Overview
Problem
Existing methods for integrating and querying biological data are inefficient and siloed, requiring significant manual effort and resource consumption, and lack a scalable framework to unify and update relationships between biological entities.
Innovation Solution
A system utilizing language processing neural networks and graph neural networks to generate, update, and query biological data graphs, enabling efficient integration and querying of biological entities and their relationships, with embeddings generated to maintain relational structure and facilitate fast query responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to integrate and query biological data, then manual effort and resource consumption are required, but the process is inefficient and not scalable
Solution Approach 1:
The patent replaces manual mechanical data integration processes with automated machine learning models. Language processing neural networks automatically extract relationships from textual biological data, and graph neural networks automatically query and traverse biological data graphs, eliminating the need for manual data processing and significantly improving productivity while reducing time loss.
Solution Approach 2:
The system enables self-service through automated processes where the machine learning models independently perform data extraction, relationship identification, and query processing. The graph neural network automatically traverses and queries the biological data graph without human intervention, allowing the system to serve itself and eliminating manual effort requirements.
2Productivity
If a scalable framework is implemented to unify biological data, then resource consumption is reduced, but system complexity increases
Solution Approach 1:
The patent segments the complex biological data processing system into distinct functional components: language processing neural networks for data extraction and relationship identification, graph neural networks for query processing and graph traversal, and a biological data graph for storing biological entities and relationships. This segmentation allows each component to be optimized independently while working together to provide fast query responses.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation layer between the biological data graph and query processing. Embeddings convert complex graph structures into compact vector representations that can be efficiently processed by machine learning models, acting as a mediator that simplifies the interaction between data storage and query processing while maintaining the full relational structure information.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a query, processing a textual representation of the query using a language processing neural network to generate an embedding of the query, generating a response to the query using: (i) the embedding of the query, and (ii) graph data representing a biological data graph comprising a set of nodes and a set of edges, wherein each node represents a respective biological entity, each edge connects a respective pair of nodes in the biological data graph and represents a relationship between a pair of biological entities, and each edge in the biological data graph is associated with a respective edge embedding representing a set of textual data describing the relationship represented by the edge, and the edge embeddings are generated using the language processing neural network, and outputting the response to the query.