Semantic Network Therapy Discovery for Bioactive Associations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for automated therapy and bioactive discovery are inefficient and lack the ability to derive meaningful associations and action pathways between chemical and biological concepts from scientific publications, hindering targeted research and development of therapies.
Innovation Solution
A method utilizing a vector space model and semantic network to compile and analyze chemical and biological concepts from scientific publications, deriving association scores and action characteristics, and generating hypotheses based on user queries to streamline therapy development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated therapy discovery methods are used, then productivity is improved, but the ability to derive meaningful associations and action pathways between chemical and biological concepts deteriorates
Solution Approach 1:
The patent combines multiple data sources including scientific publications, chemical databases, and biological databases into a unified semantic network. This merging allows the system to maintain comprehensive associations and action pathways between chemical and biological concepts while achieving automated therapy discovery, resolving the contradiction between productivity improvement and information loss.
2Measurement precision
If semantic network analysis is performed to derive associations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a semantic network as an intermediary layer between raw data sources and therapy discovery outputs. This semantic network serves as a mediator that structures complex relationships between chemical and biological concepts, enabling precise measurement of associations and action pathways while managing system complexity through organized representation.
3Loss of information
If comprehensive analysis of scientific publications is conducted, then loss of information is reduced, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and structuring scientific publications into a semantic network before actual therapy discovery queries. This preliminary organization of information into standardized formats with predefined associations and action pathways enables rapid retrieval and analysis during therapy discovery, reducing the time loss that would otherwise occur during comprehensive analysis.
Data Source
AI summary
A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.