Multi-Omic Lung Nodule Classification for Early Cancer Detection
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Solution Overview
Problem
There is a need for accurate and early detection of disease states such as cancer to improve treatment and prognosis.
Innovation Solution
Multi-omic methods involving the generation of proteomic and nucleic acid sequencing measurements from biofluid samples, using enrichment protocols and classifiers with high performance characteristics, such as an AUC of 0.9, to evaluate disease states and select appropriate cancer therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single-omic data is used for disease detection, then the detection method is simpler, but the detection performance is lower
Solution Approach 1:
The patent combines multiple omic data types (proteomic, metabolomic, transcriptomic, and/or genomic data) into an integrated multi-omic dataset for disease detection. This merging of different biological data layers enables the classifier to achieve superior detection performance (AUC ≥ 0.9) by leveraging complementary information from various molecular levels, thereby resolving the contradiction between detection performance and method complexity.
2Measurement precision
If enrichment protocols are applied to proteins, then the sensitivity of protein detection is improved, but the processing time and complexity increase
Solution Approach 1:
The patent applies enrichment protocols as a preliminary step before proteomic analysis to concentrate target proteins from biofluid samples. This preliminary enrichment action improves the sensitivity and precision of subsequent protein detection by increasing the concentration of low-abundance proteins, thereby resolving the contradiction between detection sensitivity and processing efficiency.
3Measurement precision
If multi-omic data integration is performed, then the disease detection accuracy is enhanced, but the data processing complexity increases
Solution Approach 1:
The patent introduces a classifier as an intermediary computational tool that integrates multiple omic data types (proteomic, metabolomic, transcriptomic, and/or genomic data) to evaluate disease state. This classifier mediates the complex data integration process by applying machine learning algorithms to synthesize information from various data sources, thereby enhancing disease detection accuracy (AUC ≥ 0.9) while managing the complexity through automated computational methods.
Data Source
AI summary
Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic 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. The methods may include assessing whether a nodule, mass, or cyst is cancerous.