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Related Experiment Video

Updated: Jun 30, 2025

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial

Yujian Lee¹, Yongqi Xu², Peng Gao³

¹Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China; Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region; Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.

Journal of Molecular Biology

|March 20, 2024

Summary

This summary is machine-generated.

We developed TENET, a novel graph neural network, to accurately reconstruct cell-cell interactions (CCI) from spatial transcriptomics data. TENET significantly improves reconstruction accuracy, even with noisy or sparse data, outperforming existing methods.

Keywords:

cell-cell interaction network reconstruction deep learning gene regulatory network graph neural network spatial transcriptomics

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Last Updated: Jun 30, 2025

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Computational biology
Bioinformatics
Systems biology

Background:

Cellular communication is orchestrated by signaling molecules forming cell-cell interaction (CCI) networks, crucial for tissue function.
Spatial transcriptomics (ST) data enables the study of CCI, but shallow neural networks struggle with noisy and sparse data.
Existing methods for CCI reconstruction from ST data yield suboptimal results due to limitations in handling data complexities.

Purpose of the Study:

To propose a novel method, TENET (Triple-Enhancement based Graph Neural Network), for accurate and comprehensive CCI reconstruction from ST data.
To address the limitations of shallow neural networks in reconstructing CCI from noisy and sparse ST data.
To enhance the capture of valuable biological features and improve denoising capabilities in CCI network inference.

Main Methods:

Developed TENET, a graph neural network incorporating three progressive enhancement mechanisms for cumulative feature extraction and noise reduction.
Integrated knowledge across enhancement stages to guide the decoding and reconstruction of CCI networks.
Validated TENET on both synthetic and real-world spatial transcriptomics datasets.

Main Results:

TENET demonstrated superior performance in CCI reconstruction compared to state-of-the-art methods.
Achieved an average improvement of 9.61% in Average Precision (AP) and 7.32% in Area Under the Receiver Operating Characteristic (AUROC).
The method effectively handles sparse connections and noise inherent in spatial transcriptomics data.

Conclusions:

TENET provides a robust and accurate approach for reconstructing cell-cell interaction networks from spatial transcriptomics data.
The proposed triple-enhancement mechanism significantly enhances the ability to capture biological signals and mitigate noise.
TENET represents a substantial advancement in computational methods for analyzing cellular communication in tissues.