Cumulant-Enabled Multi-Omics Embeddings for Higher-Order Label Prediction
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Solution Overview
Problem
Current methods struggle to accurately predict class labels of multimodal data due to the difficulty in capturing higher-order relationships among various features in high-dimensional spaces, leading to increased computational complexity and intractable problems.
Innovation Solution
A cumulant-enabled multi-omics neural network (CumiNN) transforms high-dimensional unlabeled multimodal data into lower-dimensional embeddings, computes synthetic representations of higher-order joint cumulants, and predicts class labels using a trained neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-dimensional multimodal data is processed directly to predict class labels, then the model can capture all original features, but computational complexity increases and the problem becomes intractable
Solution Approach 1:
The patent applies dimensionality change by transforming high-dimensional multimodal data into lower-dimensional embeddings through a neural network. This embedding transformation reduces the complexity of the data representation while preserving essential information, making the subsequent computation of higher-order joint cumulants and class label prediction tractable. The lower-dimensional embedding space allows efficient computation that would be intractable in the original high-dimensional space.
Solution Approach 2:
The patent extracts higher-order joint cumulant information from the high-dimensional data by computing cumulants of the lower-dimensional embedding. This extraction process isolates the essential higher-order relationships and structural information from the complex high-dimensional data, separating the computationally intensive part (cumulant computation) from the data representation, thereby making the overall problem solvable.
2Measurement precision
If higher-order joint cumulants are computed from high-dimensional data, then complex relationships can be captured, but computational resources required increase significantly
Solution Approach 1:
The patent reduces computational resource requirements by first transforming high-dimensional data into lower-dimensional embeddings, then computing higher-order joint cumulants in this reduced dimensionality. This dimensional reduction dramatically decreases the computational complexity and energy consumption required for cumulant computation while still capturing the essential higher-order relationships present in the original data.
Solution Approach 2:
The patent performs preliminary action by computing the lower-dimensional embedding before computing the higher-order joint cumulants. This preliminary embedding transformation prepares the data in a computationally efficient representation that enables subsequent cumulant computation to be feasible, separating the heavy lifting of dimensionality reduction from the cumulant calculation.
3Device complexity
If standard neural networks are used for class label prediction, then the model is simple, but it fails to capture higher-order relationships among features
Solution Approach 1:
The patent merges the computation of higher-order joint cumulants with the neural network architecture itself. By integrating cumulant computation into the neural network layers, the model combines the simplicity of neural networks with the ability to capture higher-order relationships. The neural network processes both the data and the cumulant information through unified layers, achieving enhanced prediction accuracy without requiring entirely separate complex systems.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for capturing higher-dimensional relationships between multimodal data features is provided. The present invention may include retrieving high-dimensional unlabeled multimodal data; processing the high-dimensional unlabeled multimodal data through a trained cumulant-enabled multi-omics neural network (CumiNN) to transform the high-dimensional unlabeled multimodal data into a lower-dimensional embedding; processing the lower-dimensional embedding further through the trained CumiNN to compute a plurality of synthetic representations of higher-order joint cumulants; and processing the plurality of synthetic representations of the higher-order joint cumulants further through the trained CumiNN to predict class labels of the higher-order joint cumulants.