Cell Ranking From Multi-Marker OMICS for Easier Interpretation
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Solution Overview
Problem
Ribonucleic acid (RNA) analysis and OMICS sequencing produce outputs that are difficult to interpret and may include bias, requiring significant user expertise and time, leading to potential misinterpretation and incorrect results.
Innovation Solution
Systems and methods for scoring and ranking cells based on multi-marker OMICS analysis, using sequencers to determine cell enrichment and correlation with selected characteristics, enabling easy interpretation and personalized product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OMICS sequencing analysis is performed, then comprehensive cell data is obtained, but the output becomes difficult to interpret and requires high user expertise
Solution Approach 1:
The patent introduces an intermediary computational system that processes raw OMICS sequencing data and transforms it into simplified, interpretable outputs. This intermediary layer includes algorithms that automatically interpret complex data patterns, reducing the need for expert user interpretation while maintaining analytical accuracy.
Solution Approach 2:
The patent replaces manual expert interpretation (mechanical human analysis) with automated computational algorithms. The system uses machine learning models and statistical algorithms to automatically interpret OMICS data, substituting the need for human expertise with automated intelligent systems that provide consistent, scalable interpretation.
2Measurement precision
If expert users manually interpret OMICS analysis output, then detailed cell characteristics can be identified, but significant time and effort are required
Solution Approach 1:
The patent enables the system to perform self-service interpretation of OMICS data through automated algorithms. The computational system independently processes and interprets complex cell data without requiring manual expert intervention, automatically identifying cell characteristics and generating results that would traditionally require extensive human analysis time.
Solution Approach 2:
The patent substitutes manual expert interpretation with automated computational algorithms that can process and interpret OMICS data rapidly. The system uses machine learning models to automatically identify cell characteristics, replacing the time-consuming manual analysis process with efficient automated computation.
3Measurement precision
If manual cell analysis is performed by users, then cell characteristics can be identified, but user bias may cause incorrect interpretations
Solution Approach 1:
The patent replaces manual human interpretation with automated computational algorithms that eliminate user bias. The system uses objective mathematical models and statistical algorithms to analyze cell characteristics, ensuring consistent and reproducible results without the subjectivity inherent in manual expert interpretation.
Solution Approach 2:
The patent implements feedback mechanisms where the automated system continuously refines its analysis based on validated results and established biological knowledge databases. The system incorporates feedback loops that compare automated interpretations against known biological patterns, ensuring reliability and consistency while eliminating user bias through objective algorithmic processing.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for ranking cells in reference to a cell or biological signature associated with skin, hair or scalp health. In an embodiment, the method may include identifying cells of interest of a subject. The method may or may not include iteratively applying an external stimuli to induce a pathological state different portions of cells. The method may include analyzing, via an OMICS sequencer, each portion of the cells to produce a multi-marker OMICS analysis. The method may include determining a rank for each of the cells for one or more selected characteristics based on application of the multi-marker OMICS analysis to an expression model for the one or more selected characteristics. The method may include determining a next action based on a the rank for each of the cells.