Knowledge Graphs for Multimodal Artificial Brain Perfusion Analysis
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Solution Overview
Problem
Existing machine learning models struggle to efficiently process and analyze the rich, multi-modal, and heterogeneous data generated by artificial perfusion experiments on mammalian brains, which are crucial for understanding brain function and drug effects, due to the challenges of data storage, querying, and aligning data from diverse modalities.
Innovation Solution
A knowledge graph is constructed to represent artificial perfusion experiment data, using graph neural networks to perform prediction tasks without explicit alignment of multi-modal data, allowing for efficient retrieval and analysis of data relevant to user queries, and accommodating new experiments and modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing machine learning models are used to process artificial perfusion experiment data, then prediction tasks can be performed, but the models struggle to efficiently process and analyze multi-modal and heterogeneous data due to challenges of data storage, querying, and aligning data from diverse modalities
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary data structure that mediates between heterogeneous data sources and machine learning models. The knowledge graph standardizes and integrates multi-modal data (imaging, omics, physiological measurements) into a unified framework with standardized node types and relationships, enabling efficient querying and processing without requiring complex alignment operations between different data modalities
Solution Approach 2:
The patent transforms heterogeneous experimental data into a standardized parameter framework by mapping diverse data types to consistent node attributes and relationship types in the knowledge graph. This parameter standardization allows machine learning models to process various data modalities uniformly, improving processing efficiency while reducing alignment complexity
2Loss of information
If multi-modal data from diverse modalities is collected to comprehensively characterize brain state, then data completeness is improved, but data storage and querying complexity increases
Solution Approach 1:
The patent segments multi-modal data into distinct standardized node types within the knowledge graph (e.g., imaging nodes, omics nodes, physiological measurement nodes), each with defined attributes and relationships. This segmentation allows comprehensive data collection while organizing information into manageable, queryable units that reduce storage and retrieval complexity
Solution Approach 2:
The knowledge graph provides a universal data structure that can accommodate multiple data modalities through standardized node and relationship types. This multi-functional framework enables the system to store and query diverse data types (imaging, omics, physiological measurements) using a single unified approach, reducing overall system complexity
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting data characterizing a state or biological function of a brain. In one aspect, a method comprises obtaining, for each of a plurality of mammalian brains, respective multi-modal experimental data that characterizes a state of the mammalian brain during or after an artificial perfusion of the mammalian brain by an electromechanical perfusion device; generating a knowledge graph that jointly represents the multi-modal experimental data characterizing the plurality of mammalian brains, the knowledge graph comprising a set of nodes that each represent elements of the multi-modal experimental data and a set of edges that each represent a relationship between a respective pair of nodes; receiving a query from a user; and generating a response to the query based at least in part on the knowledge graph.