Multiple Instance Learning for Genetic Variant Identification
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Solution Overview
Problem
Current methods for identifying genetic diseases and discovering disease-associated genetic variants are limited by the need for instance labels, which restricts the application of single instance learning, and existing approaches do not effectively utilize multiple instance learning to determine if a patient's disease is genetic and identify the causative variant without labeled data.
Innovation Solution
A system and method utilizing a multiple instance learning model with an attention mechanism to process genetic variant information, generating attention weights to determine the presence of a genetic disease and identify disease-associated variants without requiring instance labels, by embedding instances into low-dimensional vectors and using these weights to predict the disease causation and retrain the model for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single instance learning is used to identify disease-associated genetic variants, then the model can process individual variants, but it requires instance labels which are difficult to obtain and limit practical application
Solution Approach 1:
The patent segments the learning task into two levels: bag-level (patient-level) learning using multiple instance learning to determine genetic disease presence, and instance-level (variant-level) analysis using attention weights to identify specific disease-associated variants. This segmentation allows the model to first learn from unlabeled patient data and then pinpoint specific variants without requiring variant-level labels.
Solution Approach 2:
The patent introduces attention weights as an intermediary mechanism that bridges the gap between multiple instance learning and single instance learning. The attention mechanism generates weights for each genetic variant within a patient's data, effectively creating instance-level information from bag-level learning without requiring instance labels, thus resolving the contradiction between needing precise variant identification and lacking labeled data.
2Ease of manufacture
If multiple instance learning is used to determine whether a disease is genetic, then the model can work without instance labels, but it cannot identify specific disease-associated genetic variants
Solution Approach 1:
The patent implements a feedback mechanism where the multiple instance learning model's predictions at the bag level feed into the attention mechanism, which in turn generates instance-level attention weights. These attention weights provide feedback information about which specific variants are most relevant, allowing the model to identify disease-associated variants even though the primary MIL training used only bag-level labels.
Solution Approach 2:
The attention mechanism serves as an intermediary that extracts instance-level information (specific variant importance) from the bag-level multiple instance learning framework. This intermediary component recovers the lost specific variant information by generating attention weights that highlight disease-associated variants without requiring instance-level training labels.
3Measurement precision
If traditional comparison methods are used to identify genetic variants by comparing cases and controls, then disease-associated variants can be identified, but the process is time-consuming and less efficient
Solution Approach 1:
The patent replaces the traditional mechanical comparison process (manual or algorithmic comparison of cases and controls) with a neural network-based multiple instance learning system. The MIL model with attention mechanism automatically processes and compares genetic variant data, learning patterns and associations that would be difficult to detect through traditional comparison methods, thereby improving both speed and accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of the analysis by moving from traditional statistical comparison methods to deep learning-based multiple instance learning. This parameter change enables the system to handle high-dimensional genetic data more efficiently, automatically learning relevant features and interactions without requiring explicit comparison protocols, thus improving productivity while maintaining or enhancing identification accuracy.
Data Source
AI summary
The present disclosure provides a system configured to identify a genetic disease and discover a disease-associated genetic variant, the system including a multiple instance learning model unit configured to derive identification of a genetic disease of a patient and discovery of a disease-associated genetic variant together using a multiple instance learning model configured to learn instances which are genetic variant information of the patient and a bag of the instances as input data and process, as a bag label, whether a disease of the patient is a genetic disease caused by a genetic variant.