Lupus SNP Classification Using Multi-Omics Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately identify specific phenotypes, such as disease states or medication responses, using machine learning algorithms due to the complexity and heterogeneity of high-scale disease and treatment data, particularly in the context of lupus conditions.
Innovation Solution
A method utilizing machine learning algorithms, including elastic generalized linear model classifiers, k-nearest neighbors classifiers, and random forest classifiers, to analyze nucleic acid sequencing, transcriptome, and other omics data, with specific penalties and K-values, to identify records associated with a specific phenotype with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning algorithms are applied to high-scale disease and treatment data, then prediction and analysis speed is improved, but data complexity and heterogeneity increase the difficulty of accurate phenotype identification
Solution Approach 1:
The patent segments the data analysis process into distinct phases: receiving and storing data records, applying machine learning algorithms to trained datasets, and generating phenotype predictions. This segmentation allows the system to handle high-scale data systematically by processing it in manageable chunks through multiple computational steps, thereby maintaining speed while improving accuracy through structured analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and storing data records in a database before applying machine learning algorithms. The system pre-organizes data from multiple sources (genomic, proteomic, metabolomic) and prepares trained datasets that can be quickly queried. This preliminary organization enables fast predictions while ensuring accurate phenotype identification by having data ready in optimized formats.
2Adaptability or versatility
If multiple types of omics data are integrated for comprehensive analysis, then phenotypic coverage is improved, but data format diversity and source heterogeneity increase system complexity
Solution Approach 1:
The patent implements a universal data processing system that can handle multiple types of omics data (genomic, proteomic, metabolomic) through a single integrated platform. The system uses a unified database structure and standardized machine learning algorithms that can process different data formats and sources, thereby achieving comprehensive phenotypic coverage without proportionally increasing system complexity.
Solution Approach 2:
The patent employs parameter changes by adjusting machine learning algorithm parameters and data processing parameters to optimize performance across different data types. The system can modify processing parameters based on the specific characteristics of genomic, proteomic, or metabolomic data, allowing comprehensive multi-omics analysis while maintaining manageable system complexity through parameter optimization.
3Quantity of substance
If machine learning algorithms process high-scale data from multiple sources, then analysis comprehensiveness is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary data processing and storage actions that optimize future computations. By pre-processing data from multiple sources and storing it in an optimized database structure, the system reduces the computational burden during actual analysis. This preliminary action enables comprehensive data processing while minimizing real-time computational resource consumption.
Solution Approach 2:
The patent creates copies of processed data and trained models that can be efficiently queried and analyzed. By maintaining optimized copies of data records and pre-computed results, the system can handle large data volumes without proportionally increasing computational resources during analysis, as the system can leverage pre-processed information rather than processing raw data from scratch.
Data Source
AI summary
The present disclosure provides systems and methods for machine learning classification and assessment of disease based on gene expression data. In an aspect, a method for determining a disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of disease-associated genomic loci; (b) computer processing the data set to determine the disease state of the subject; and (c) electronically outputting a report indicative of the disease state of the subject. In some embodiments, the plurality of disease-associated genomic loci comprises single nucleotide polymorphisms (SNPs). In some embodiments, the disease comprises a lupus condition. In some embodiments, the disease comprises cardiovascular disease (CVD).