18411996. Systems and Methods for Cell Typing using GenoMaps simplified abstract (THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY)
Contents
Systems and Methods for Cell Typing using GenoMaps
Organization Name
THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY
Inventor(s)
Md Tauhidul Islam of Stanford CA (US)
Systems and Methods for Cell Typing using GenoMaps - A simplified explanation of the abstract
This abstract first appeared for US patent application 18411996 titled 'Systems and Methods for Cell Typing using GenoMaps
The abstract describes a system and method for cell typing using single cell ribonucleic acid sequencing (scRNA-seq) data to generate a two-dimensional image called GenoMap, analyze gene-gene interactions, classify cells using a convolutional neural network (CNN), and display the cell classification.
- Utilizes scRNA-seq data to create a GenoMap image representing gene-gene interactions.
- Employs a CNN to classify cells based on the GenoMap image.
- Provides a display of the cell classification results.
Potential Applications: - Biomedical research for cell classification and understanding gene interactions. - Precision medicine for personalized treatment based on cell types. - Drug discovery to identify targets for new therapies.
Problems Solved: - Enables accurate and efficient cell typing using scRNA-seq data. - Automates the cell classification process for high-throughput analysis.
Benefits: - Enhances understanding of cellular heterogeneity. - Facilitates targeted therapies and personalized medicine. - Accelerates drug discovery and development processes.
Commercial Applications: Title: "Innovative Cell Typing System for Biomedical Research and Precision Medicine" This technology can be applied in research institutions, pharmaceutical companies, and healthcare settings for cell classification, drug development, and personalized medicine, potentially revolutionizing the field.
Questions about Cell Typing: 1. How does this technology improve the accuracy of cell classification compared to traditional methods? 2. What are the implications of using a CNN for cell typing in terms of scalability and efficiency?
Original Abstract Submitted
Systems and methods for cell typing in accordance with embodiments of the invention are illustrated. One embodiment includes a cell typing system, including a processor, and a memory, the memory containing a cell typing application that configures the processor to obtain single cell ribonucleic acid sequencing (scRNA-seq) data generated from a single cell, generate a two-dimensional (2D) image includes a grid of pixels referred to as a GenoMap, where each pixel describes a gene-gene interaction based upon the scRNA-seq data, provide the 2D image to a convolutional neural network (CNN), obtain a cell classification of the single cell from the CNN, and provide the cell classification via a display.
