Multi-Omics Biomarker Screening for Early Cancer Detection
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Solution Overview
Problem
Current methods lack the accuracy and sensitivity for early detection of diseases such as cancer, particularly pancreatic and lung cancer, using biofluid samples.
Innovation Solution
A multi-omics approach utilizing proteomics, metabolomics, lipidomics, transcriptomics, fragmentomics, and methylomics data from biofluid samples, combined with machine learning classifiers, to identify specific biomarkers that distinguish between healthy and cancerous states, achieving high sensitivity and specificity in disease detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-omics methods are used for disease detection, then the approach is simple and cost-effective, but the accuracy and sensitivity for early detection are insufficient
Solution Approach 1:
The patent combines multiple omics layers (proteomics, metabolomics, lipidomics, transcriptomics, fragmentomics, and methylomics) into a unified multi-omics detection system. This integration allows comprehensive analysis of different biological molecules simultaneously, significantly improving detection accuracy and sensitivity for early cancer diagnosis while managing complexity through systematic integration of data from multiple sources
Solution Approach 2:
The multi-omics database and analysis platform serves multiple functions: it stores and processes data from various omics types, enables biomarker discovery across different disease states, provides classification capabilities for various cancer types, and supports both research and clinical applications. This universal platform approach improves detection precision while consolidating complexity into a single versatile system
2Measurement precision
If multi-omics data from large populations is collected, then the biomarker identification accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex multi-omics data processing into distinct manageable components: data acquisition from various omics sources, data normalization and quality control, biomarker identification through machine learning algorithms, and validation against reference datasets. This segmentation allows systematic processing of large population data while reducing the complexity burden on any single processing step
Solution Approach 2:
The patent introduces machine learning algorithms as intermediary tools that bridge the gap between raw multi-omics data and meaningful biomarker identification. These algorithms process and analyze the complex data patterns, transforming raw data from multiple omics layers into actionable insights about disease states and biomarkers, thereby managing processing complexity while improving identification accuracy
3Reliability
If multiple types of omics data are integrated, then the comprehensive understanding of disease mechanisms improves, but the difficulty of data analysis and interpretation increases
Solution Approach 1:
The patent adds multiple dimensions of biological information by integrating different omics layers, each providing unique insights into disease mechanisms. Proteomics provides protein level information, metabolomics reveals metabolic states, lipidomics indicates membrane composition changes, transcriptomics shows gene expression patterns, fragmentomics identifies specific molecular fragments, and methylomics indicates epigenetic modifications. This multi-dimensional approach improves reliability by cross-validating findings across different biological levels while managing analysis difficulty through structured data organization
4Adaptability or versatility
If biofluid samples from diverse population characteristics are analyzed, then the biomarkers become more universally applicable, but the heterogeneity of samples increases analysis complexity
Solution Approach 1:
The patent addresses sample heterogeneity by systematically adjusting and normalizing measurement parameters across different population characteristics. The analysis methodology incorporates corrections for age, sex, race, weight, height, dietary consumption, exercise habits, activity level, and smoking status as confounding parameters. This allows the system to maintain high biomarker applicability across diverse populations while managing the complexity of processing heterogeneous samples through standardized normalization protocols
Data Source
AI summary
Described herein are methods such as multi-omics methods for assessing a disease. The multi-omics methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. Also described herein are multi-omics databases and methods of using them.