AI Single-Cell Tumor Analysis for Phenotype Heterogeneity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current clinical methods classify tumor gene phenotypes based on a sum of all genes from cancer, immune, and stromal cells, leading to limitations in selecting appropriate treatments for cancer cells with different characteristics within the same tumor, and existing AI applications lack precision in analyzing tumor samples on a single cell unit.
Innovation Solution
A single cell analysis device using artificial intelligence to acquire, preprocess, classify, and predict tumor phenotypes from RNA expression, enabling accurate classification and personalized treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification is made based on sum of all genes from cancer, immune, and stromal cells, then overall tumor characterization is achieved, but precision in identifying cancer cell-specific phenotypes deteriorates
Solution Approach 1:
The patent segments the tumor sample into individual single cells and analyzes their gene expression profiles separately. Instead of bulk analysis that mixes all cell types, the system isolates and profiles each cell individually, then aggregates results to achieve both precision in cancer cell identification and comprehensive tumor characterization.
Solution Approach 2:
The patent extracts and analyzes only the relevant cancer cell gene expression data from the complex tumor microenvironment. By selectively focusing on cancer cell-specific markers and phenotypes while separating them from immune and stromal cell signals, the system achieves precise cancer cell characterization without being overwhelmed by total tumor complexity.
2Productivity
If AI technology is applied to analyze tumor samples, then analysis efficiency is improved, but precision in single-cell level analysis deteriorates
Solution Approach 1:
The patent performs preliminary preprocessing of single-cell data including quality control, normalization, and feature selection before applying AI algorithms. This preliminary action ensures that the AI receives high-quality, standardized input data, enabling both efficient processing and precise single-cell level analysis without losing critical biological signals.
Solution Approach 2:
The patent introduces an intermediary layer between single-cell data and AI analysis that includes data preprocessing, feature extraction, and quality filtering. This intermediary layer bridges the gap by preparing data in a format that maintains single-cell resolution information while being suitable for AI processing, thus preserving precision while gaining efficiency.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
Disclosed is a single cell analysis device for analyzing a tumor using artificial intelligence, the device including, a memory, and a processor, and the processor is configured to acquire single cell data on a tumor of a particular patient through the communication part; pre-process the single cell data on the tumor through a data pre-processing module; classify a type of the single cell based on the data pre-processed by a cell type defining module; predict a phenotype derivable from RNA expression of the tumor of the particular patient through a single-cell state predicting module materialize heterogeneity of the tumor of the particular patient through the tumor analyzing module; and report and provide an analysis result of the tumor of the particular patient through an analysis result generating module.