Disease Mapping Neural Networks for Unknown Disease Clustering
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Solution Overview
Problem
Current disease diagnostic models lack the ability to accurately and efficiently identify both known and unknown diseases in populations, particularly in applications such as blood banks, nursing homes, and bio-surveillance systems, due to their limited predictive capabilities and reliance on biased data sets.
Innovation Solution
An AI system using machine learning algorithms, specifically an electronic neural network trained on peptide sequence and binding value pair data sets, applies clustering algorithms like UMAP, PCA, and k-means to generate disease maps that unsupervisedly cluster disease states, enabling identification of known and unknown diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current disease diagnostic models are used, then disease detection can be performed, but the ability to accurately and efficiently identify both known and unknown diseases is limited due to biased data sets and limited predictive capabilities
Solution Approach 1:
The system performs preliminary unsupervised clustering on antibody binding data to generate disease maps before specific disease identification is needed. This pre-processing organizes the data structure in advance, enabling both known and unknown diseases to be identified more accurately when clinical questions arise, without requiring retraining or reprocessing of raw data.
Solution Approach 2:
The patent transforms high-dimensional antibody binding data into reduced-dimensional disease maps through clustering algorithms. This dimensional transformation preserves essential disease-related patterns while removing noise and redundancy, enabling better visualization and interpretation of both known and unknown disease states in a compressed feature space.
2Reliability
If traditional diagnostic models with biased data sets are used, then training can be completed with available data, but predictive performance and robustness to noise are limited
Solution Approach 1:
The system performs self-service through unsupervised clustering that automatically discovers disease patterns without requiring labeled training data or manual intervention. The algorithm independently organizes antibody binding data into disease maps, eliminating the need for biased pre-labeled datasets and reducing dependency on external annotation resources while improving predictive reliability.
Solution Approach 2:
The patent changes the fundamental parameter from labeled disease categories to unsupervised cluster structures. By transforming the data representation from predefined disease labels to emergent cluster patterns based on antibody binding similarities, the system achieves greater robustness to noise and improved generalization to unknown diseases without increasing model complexity.
3Adaptability or versatility
If clustering algorithms are applied to weight and bias values of trained neural networks, then disease maps can be generated to cluster disease states, but computational processing time increases
Solution Approach 1:
The system extracts essential disease-related information from trained neural network weight and bias values through clustering algorithms. By taking out only the critical pattern information needed for disease state identification rather than processing entire datasets repeatedly, the system achieves efficient generation of disease maps that can be quickly applied to new cases without redundant computational overhead.
Data Source
AI summary
Provided herein are computer-implemented methods of generating a disease map of a population. In some embodiments, the methods include applying a clustering algorithm to a set of weight and bias values of a trained electronic neural network to generate the disease map of the population. In some embodiments, the electronic neural network has been trained on training data that comprises representations of peptide sequence and binding value pair data sets obtained from reference subjects in the population in which a given peptide sequence and binding value pair data set comprises peptide sequence information and peptide binding values of antibodies to peptides that comprises the peptide sequence information. In some embodiments, the antibodies are from a sample obtained from a given reference subject in the population and are indicative of one or more disease states. Related systems, computer readable media, and additional methods are also provided.