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Types of Genetic Transfer Between Organisms

Types of Genetic Transfer Between Organisms

Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.

Types of Genetic Transfer Between Organisms

Types of Genetic Transfer Between Organisms

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Updated: Nov 1, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.

Qianqian Song^1,2, Jing Su^3,4, Wei Zhang^5,6

¹Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston Salem, NC, USA.

Nature Communications

|June 23, 2021

Summary

This summary is machine-generated.

This study introduces a novel graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), for robust label transfer in single-cell omics data. scGCN significantly improves accuracy across diverse datasets, overcoming limitations of existing methods.

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Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

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Published on: February 24, 2021

Area of Science:

Genomics
Computational Biology
Artificial Intelligence

Background:

Single-cell omics data is rapidly expanding, necessitating effective methods for label transfer.
Current label transfer techniques face challenges due to data heterogeneity and batch effects.
Accurate cell type annotation is crucial for exploring single-cell omics datasets.

Purpose of the Study:

To develop a robust graph artificial intelligence model for effective knowledge transfer across disparate single-cell omics datasets.
To enhance the performance of label transfer methods for single-cell omics data analysis.
To provide a scalable and accurate solution for cell type annotation.

Main Methods:

Development of a novel graph artificial intelligence model named single-cell Graph Convolutional Network (scGCN).
Benchmarking scGCN against existing label transfer methods using 30 diverse single-cell omics datasets.
Implementation of scGCN as an integrated Python software workflow.

Main Results:

scGCN demonstrated superior accuracy in label transfer compared to other methods.
The model effectively leveraged cells from different tissues, platforms, species, and molecular layers.
Consistent performance improvements were observed across all tested datasets.

Conclusions:

scGCN offers a robust and accurate solution for label transfer in single-cell omics.
The model overcomes limitations posed by data heterogeneity and extrinsic differences between datasets.
scGCN facilitates broader exploration and annotation of single-cell omics data.