Active Learning for Pairwise Perturbation Interaction Discovery
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Solution Overview
Problem
Conventional systems for predicting perturbation impact on cell behavior suffer from inaccuracies, inefficiencies, and operational inflexibility, particularly in identifying complex biological interactions that only manifest from double perturbation experiments, due to reliance on single perturbation experiments and inefficient brute-force searches.
Innovation Solution
The multi-perturbation interaction system utilizes machine learning to generate individual and pairwise perturbation embeddings, comparing them to identify biological interactions and employs active learning to efficiently select perturbation pairs for further exploration, reducing the need for exhaustive experimentation through active-matrix completion algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems rely on single perturbation experiments and brute-force searches, then operational simplicity is maintained, but accuracy in identifying biological interactions deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediaries that learn to predict perturbation effects from single perturbation data. These models serve as mediators between simple single-perturbation experiments and complex interaction identification, enabling accurate prediction of double perturbation effects without performing all possible double perturbation experiments, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent creates virtual copies of perturbation effects through machine learning predictions. Instead of physically performing all possible double perturbation experiments, the system generates predicted embeddings that copy the expected effects of double perturbations based on learned patterns from single perturbations, thereby achieving accurate interaction identification without the experimental complexity
2Loss of information
If exhaustive double perturbation experiments are performed, then completeness of interaction identification is achieved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by training machine learning models on single perturbation data before attempting to identify interactions. The models learn the underlying patterns and relationships in advance, enabling them to accurately predict double perturbation effects without actually performing all the time-consuming double perturbation experiments, thus achieving complete interaction identification with minimal time loss
Solution Approach 2:
The patent applies partial action by performing only a subset of double perturbation experiments that are most informative for model training and validation. The machine learning model then generalizes from this partial data to predict the effects of all possible double perturbations, achieving complete interaction identification without the excessive time required for exhaustive experimentation
3Loss of information
If all possible perturbation pairs are explored, then completeness of interaction discovery is achieved, but computational cost increases
Solution Approach 1:
The patent performs preliminary computation by training machine learning models on single perturbation data to learn the mapping between perturbations and cellular responses. This preliminary learning phase enables the system to predict double perturbation effects without performing computationally expensive calculations for all possible perturbation pairs, thus achieving complete interaction discovery with reduced computational cost
Solution Approach 2:
The patent creates computational copies of double perturbation effects through machine learning predictions. Instead of performing actual computational simulations or experiments for all perturbation pairs, the system generates predicted embeddings that copy the expected outcomes, achieving complete interaction discovery without the prohibitive computational cost of exhaustive exploration
4Ease of operation
If single perturbation experiments are used, then ease of operation is maintained, but measurement precision of biological interactions deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediaries that translate simple single perturbation data into predictions of complex interaction effects. The models serve as mediators that bridge the gap between easy-to-conduct single perturbations and the precision needed for interaction identification, maintaining ease of operation while achieving high measurement precision through the learned predictions
Solution Approach 2:
The patent creates a universal machine learning model that can predict effects of any perturbation combination based on single perturbation training data. This multi-functional model serves both as a simple experimental tool (accepting single perturbation inputs) and as a precise interaction identifier (predicting double perturbation effects), thereby resolving the contradiction between ease of operation and measurement precision
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that a implement a framework for active learning to discover pairwise interactions via representation learning. Indeed, in one or more implementations, the disclosed systems generate a first individual perturbation embedding from a first representation of a first cell exposed to a first perturbation and a second individual perturbation embedding, from a second representation of a second cell exposed to a second perturbation. For instance, the disclosed systems combine the first individual perturbation embedding and the second individual perturbation embedding to determine a predicted pairwise embedding. Moreover, in some instances, the disclosed systems generate a pairwise embedding from a representation of a cell exposed to both the first and second perturbation. Additionally, from comparing the predicted pairwise embedding with the pairwise embedding, the disclosed systems generate a measure of biological interaction of the first and second perturbation.