Clinical-Phenomics Causal Discovery for Gene-Outcome Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems face challenges in accurately and efficiently generating causal relationships between genes and clinical outcomes due to high dimensionality, noise in clinical data, and rigid reliance on observed data, leading to inefficiencies and operational inflexibility.
Innovation Solution
A causal discovery system utilizing phenomic image embeddings, clustering models, machine learning classification models, and explainability models to identify gene targets and generate causal predictions by filtering data biologically and flexibly combining phenomics and clinical observation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems utilize computational models to parse through clinical data and map clinical features to specific diseases, then relationships between health abnormalities and clinically observed factors can be determined, but the systems experience reduced accuracy, efficiency, and operational flexibility due to high dimensionality and noise in clinical data
Solution Approach 1:
The patent segments the complex causal discovery process into multiple distinct modules: phenomic data processing module, clinical data processing module, feature selection module, and causal discovery module. Each module handles specific aspects of the data independently, reducing the complexity of the overall system while maintaining accuracy in mapping relationships between clinical features and diseases.
2Productivity
If conventional systems rely on observed clinical data to map relationships between clinical features and diseases, then causal predictions can be generated, but the systems experience reduced efficiency and operational flexibility due to rigid reliance on observed data and high dimensionality
Solution Approach 1:
The patent applies preliminary action by performing feature selection and dimensionality reduction before the main causal discovery process. The system pre-processes both phenomic and clinical data to identify and retain only the most relevant features, which accelerates the subsequent causal prediction generation while maintaining adaptability to different data types and research questions.
Solution Approach 2:
The patent implements a universal causal discovery framework that can handle multiple data types (phenomic images, clinical observations, electronic health records) and generate causal predictions for various diseases and health outcomes. This multi-functional approach enhances both efficiency by using a single integrated system and operational flexibility by adapting to different research scenarios.
3Measurement precision
If conventional systems parse through large volumes of clinical data to determine relationships between health abnormalities and clinically observed factors, then mapping accuracy can be improved, but the systems experience reduced efficiency due to high dimensionality and noise in the data
Solution Approach 1:
The patent extracts and removes noise and irrelevant features from the high-dimensional clinical data through sophisticated feature selection techniques. By taking out only the most informative features related to causal relationships between phenomic and clinical data, the system maintains high mapping accuracy while dramatically reducing the time required to process the data.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that analyze gene perturbation machine learning embeddings and clinical observation data sets utilizing machine learning, explainability models, and causal discovery models to generate causal predictions between one or more genes and clinical outcomes. Indeed, in one or more implementations, the disclosed systems identify gene perturbation embeddings generated from cells exposed to perturbations. For instance, the disclosed systems select a cluster of genes from a plurality of genes by applying a clustering model to the gene perturbation embeddings. In some instances, the disclosed systems select gene targets from the cluster of genes by using a machine learning classification model trained on a plurality of features of the clinical observation data set. Moreover, in some instances, the disclosed systems generate the causal prediction from the gene targets and the clinical observation data set utilizing a causal discovery model.