Multi-omics AI Platform for Molecular Target Discovery
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Solution Overview
Problem
Existing omics-based methods for product target discovery are limited as they often rely on single data types, which can provide only a partial view of complex biochemical and physiological networks, making it difficult to define precise molecular targets accurately.
Innovation Solution
A computing system comprising an omics database and an artificial intelligence-based discovery platform that integrates multiple omics data types, such as genomics, transcriptomics, proteomics, and metabolomics, to identify groups of biologically and statistically similar variables correlated with a product target.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single omics data type is used for product target discovery, then the method is simple and easy to implement, but the view of biochemical and physiological networks is partial and incomplete
Solution Approach 1:
The patent combines multiple omics data types (genomics, transcriptomics, proteomics, metabolomics) into an integrated multi-omics analysis system. This merging of different data sources provides a comprehensive view of biochemical and physiological networks, resolving the limitation of partial information obtained from single omics approaches while maintaining analytical coherence through systematic integration.
Solution Approach 2:
The patent creates a composite analytical framework that integrates multiple omics data layers, analogous to composite materials in engineering. Each omics data type contributes unique information, and their integration produces a more robust and comprehensive understanding of biological systems than any single data type alone, thereby reducing information loss while managing complexity through structured composition.
2Productivity
If single omics data type is used for product target discovery, then the analysis process is fast and efficient, but the accuracy of defining precise molecular targets is insufficient
Solution Approach 1:
The patent merges multiple omics data types to enhance measurement precision in identifying molecular targets. By integrating genomics, transcriptomics, proteomics, and metabolomics data, the system achieves more accurate target definition than single-omics approaches, while the coordinated analysis framework maintains analytical efficiency through systematic processing of multiple data streams.
Solution Approach 2:
The patent implements a feedback mechanism where multiple omics data types mutually validate and refine target identification. Each data layer provides feedback that strengthens or refines the precision of molecular target definition, allowing the system to achieve high accuracy while managing analysis complexity through iterative refinement across data types.
3Reliability
If multiple omics data types are integrated, then the understanding of molecular changes is comprehensive and robust, but the risk of false positives increases and data complexity increases
Solution Approach 1:
The patent merges multiple omics data types in a coordinated framework that enhances discovery robustness while managing false positives. The integration of genomics, transcriptomics, proteomics, and metabolomics data provides mutual validation, where consistent findings across data types strengthen reliability and reduce false positives, while the systematic merging approach controls complexity through structured analysis.
Solution Approach 2:
The patent uses feedback mechanisms where each omics data type validates findings from other data types. This cross-validation feedback reduces false positives by requiring consistent evidence across multiple data layers, while the structured feedback loop manages data integration complexity through iterative refinement and systematic cross-referencing of findings.
4Measurement precision
If multiple omics data types are integrated, then the ability to decipher product target associations is enhanced, but the computational resources and analysis time required increase
Solution Approach 1:
The patent merges multiple omics data types in an integrated analysis framework that enhances target association detection ability. By coordinating the analysis of genomics, transcriptomics, proteomics, and metabolomics data simultaneously, the system achieves superior precision in deciphering product target associations while managing analysis time through efficient data integration and coordinated processing of multiple data streams.
Data Source
AI summary
The present disclosure relates to techniques for using multi-omics data and artificial intelligence to discover variables correlated with a product target in various domains. Particularly, aspects are directed to a computer implemented method that includes (i) identifying groups of biologically and statistically similar variables, and (ii) selecting, using various artificial intelligence techniques (e.g., machine learning models and rule based systems), groups of variables that are associated with the target feature while taking into account the groups of biologically and statistically similar variables identified in (i). The results of the various artificial intelligence techniques are then cross-referenced to refine and hierarchize a final set of variables associated with the feature of interest.