Multi-Omics Tensor Regression for Complex Disease Associations
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Solution Overview
Problem
Conventional statistical and computational approaches fall short in analyzing high-dimensional, multi-omics data due to their intricate structures, which cannot be captured by vector-based or matrix-based regression models, limiting the insights from genome-wide association studies for complex diseases.
Innovation Solution
Analyzing multi-omics data through a tensor regression model, involving data pre-processing, combining modalities into higher-order tensors, and computing associations using tensor regression to identify associations with complex diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional vector-based or matrix-based regression models are used, then the analysis is simple and computationally efficient, but the models cannot capture the intricate structures of high-dimensional multi-omics data
Solution Approach 1:
The patent transitions from vector-based (1D) and matrix-based (2D) representations to tensor-based (3D or higher-order) representations to capture the multi-dimensional structure of multi-omics data. This dimensional expansion enables the model to represent complex interactions between different omics layers (genomics, proteomics, metabolomics) that cannot be captured in lower dimensions.
Solution Approach 2:
The patent creates a composite regression framework that integrates multiple data types (omics data, clinical data, imaging data) into a unified tensor structure. This composite approach allows the model to simultaneously process heterogeneous data from multiple sources while maintaining their intricate relationships.
2Measurement precision
If genome-wide association studies are used, then single genetic marker associations can be identified, but the results are limited when complex multi-omics data are used
Solution Approach 1:
The tensor regression framework serves multiple functions simultaneously: it performs genome-wide association analysis, integrates multi-omics data, captures gene-gene interactions, and handles complex data structures. This universal approach replaces the need for separate analysis methods with a single versatile model that can process diverse data types.
Solution Approach 2:
The patent merges genome-wide association study results with multi-omics data integration in a unified tensor regression framework. This combination allows the model to leverage both the strengths of GWAS (identification of genetic markers) and multi-omics data (comprehensive biological context) to provide more robust associations.
3Loss of information
If high-dimensional multi-omics data are analyzed, then comprehensive insights into complex diseases can be obtained, but the data dimensionality and intricate structures make conventional approaches fall short
Solution Approach 1:
The patent segments the high-dimensional multi-omics data into multiple tensor modes corresponding to different omics layers and sample characteristics. This segmentation allows the complex data to be processed through structured decomposition while preserving the relationships between different data types through the tensor framework.
Data Source
AI summary
Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.