Multi-Omics Contrastive Learning for Therapeutic Association Discovery
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Solution Overview
Problem
Existing methods for analyzing complex diseases, such as Alzheimer's and Parkinson's, struggle to effectively integrate and interpret multi-omics data from various healthcare modalities like genomics, transcriptomics, and proteomics, leading to limited discovery of therapeutics due to the challenge of limited annotated data, especially for rare and complex diseases.
Innovation Solution
A contrastive multi-omics association learning (CONMOAL) platform that uses a contrastive learning approach to generate many-to-many associations between different healthcare data modalities, leveraging pre-trained models to fine-tune on genomic datasets and large biobanks, enabling interpretable associations and accelerated therapeutic discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze multi-omics data, then the analysis process is simpler, but the ability to identify meaningful associations and discover therapeutics is limited
Solution Approach 1:
The patent introduces contrastive learning as an intermediary computational framework that bridges multiple omics modalities. The contrastive loss function acts as a mediator that learns meaningful associations between different data types (genomics, transcriptomics, proteomics) by comparing positive pairs (known associations) against negative pairs (random combinations), thereby enabling accurate therapeutic discovery without requiring complex manual integration protocols
Solution Approach 2:
The system performs preliminary training on large biobank datasets with known associations before applying the model to discover new therapeutics. By pre-learning the association patterns from extensive training data, the model is prepared to accurately identify meaningful relationships in target diseases, reducing the complexity of direct analysis on limited annotated data
2Measurement precision
If more annotated data is used for training, then the therapeutic discovery accuracy improves, but the availability of such data is limited especially for rare diseases
Solution Approach 1:
The system performs preliminary training on large biobank datasets with known associations before applying the model to discover new therapeutics. By pre-learning the association patterns from extensive training data, the model is prepared to accurately identify meaningful relationships in target diseases, reducing the complexity of direct analysis on limited annotated data
Solution Approach 2:
The contrastive learning framework creates synthetic training examples by generating negative pairs from random data combinations and using data augmentation techniques. This effectively multiplies the available annotated data by creating numerous variant training samples from limited positive examples, enabling robust model training even when actual annotated therapeutic data is scarce
3Measurement precision
If contrastive learning is used to learn associations between multi-omics data, then therapeutic discovery accuracy is improved, but the computational complexity increases
Solution Approach 1:
The computational process is segmented into distinct phases: (1) data preprocessing and tokenization, (2) contrastive learning training on biobank data, (3) model validation, and (4) therapeutic discovery application. Each stage processes data independently with optimized computational requirements, avoiding the need to simultaneously handle all computational tasks at full scale
Data Source
AI summary
A plurality of data pairs are created by matching an element from a first modality with an element from a second modality. Each element from the first modality and each element from the second modality are tokenized to obtain first modality tokens and second modality tokens. A model is trained based on the plurality of data pairs, the training comprising learning a first embedding from the first modality tokens via a first attention-based encoder for the first modality and a second embedding from the second modality tokens via a second attention-based encoder for the second modality, calculating a cosine similarity between the first embedding and the second embedding for each data pair and computing a loss between predicted items and ground truth based on the cosine similarity. The predicted items with a minimal loss are validated to obtain at least one candidate therapeutic.