Biological Data Structure for Personalized Medicine Queries

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Solution Overview

Problem

Current data structuring approaches, such as object-oriented data, relational databases, hyper-graphs, Bayesian networks, and hierarchical temporal memories, fail to effectively relate knowledge and data to support personalized medicine at the molecular level, limiting their ability to efficiently and robustly utilize genomic data for individualized treatments.

Innovation Solution

A computer system maintains a biological data structure that associates unique identifiers with molecular feature and knowledge elements, inducing knowledge sub-graphs through internal and external element sets, allowing for efficient querying and retrieval of relevant data and knowledge specific to individual biological entities, such as cancer patients, by processing queries and identifying corresponding molecular features using pattern matching and neural networks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional data structuring approaches (relational databases, hyper-graphs, Bayesian networks) are used, then data storage is achieved, but the ability to effectively relate knowledge and data at the molecular level is insufficient

Engineering Contradiction:

Improveeffectiveness of relating knowledge and dataVSAvoidcomplexity of data structure

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the biological data structure into distinct components: molecular feature data, knowledge elements, internal element sets, and external element sets. This segmentation allows each component to be optimized independently while maintaining their relationships through structured references, resolving the contradiction between data relation effectiveness and structural complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a new dimensional organization by creating bidirectional relationships (internal and external element sets) that add a relational dimension beyond traditional hierarchical or flat structures. This enables efficient navigation and querying of molecular data from multiple perspectives simultaneously, improving knowledge-data relation effectiveness without proportionally increasing complexity.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Adaptability or versatility

If comprehensive molecular feature data is collected for personalized medicine, then treatment personalization is improved, but data processing efficiency decreases

Engineering Contradiction:

Improvepersonalization capabilityVSAvoiddata processing efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent performs preliminary organization of molecular feature data into structured relationships (internal and external element sets) before querying. By pre-establishing these bidirectional references during data ingestion, the system enables rapid retrieval and processing during personalized treatment analysis, resolving the contradiction between comprehensive data collection and processing efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces knowledge elements as intermediaries that bridge molecular feature data and treatment information. These intermediaries organize and contextualize raw molecular data, making it more efficient to process and analyze for personalized medicine applications by providing a structured layer between data sources and analysis queries.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If detailed molecular feature relationships are maintained, then query accuracy is improved, but memory requirements increase

Engineering Contradiction:

Improvequery accuracyVSAvoidmemory consumption

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts and stores only the essential relational information (unique identifiers and element set references) separately from the full molecular feature data. This extraction allows the system to maintain detailed relationship metadata for accurate querying while storing the actual molecular data efficiently, resolving the contradiction between query accuracy and memory consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS8898149B2Biological data structure having multi-lateral, multi-scalar, and multi-dimensional relationships between molecular features and other data

Publication Date: 2014.11.25 TRANSLATIONAL GENOMICS RESEARCH INSTITUTE

AI summary

A computer system maintains a biological data structure having molecular feature data. The system receives data elements indicating biological molecular features and knowledge elements that represent biological concepts. The system individually associates unique identifiers with the elements. For individual elements, the system maintains an internal element set of the other unique identifiers for the other elements that are directly associated with that one individual element. For the individual elements, the system maintains an external element set of the other unique identifiers for the other elements that have that one individual element in their own internal element sets. Although not required, the computer system may process a query indicating a search scope and a molecular feature for an individual biological entity, and responsively process the molecular feature and the elements based on the search scope to induce a knowledge sub-graph for the individual biological entity.