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Updated: Jan 2, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Deep learning for inferring gene relationships from single-cell expression data.

Ye Yuan¹, Ziv Bar-Joseph^1,2

¹Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings of the National Academy of Sciences of the United States of America

|December 12, 2019

Summary

This summary is machine-generated.

We developed a novel deep learning framework, convolutional neural network for coexpression (CNNC), to analyze gene expression data. CNNC effectively mines gene-gene relationships for various biological tasks, outperforming existing methods.

Keywords:

causality inference deep learning gene interactions

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Mining gene-gene relationships from expression data is crucial for understanding biological systems.
Existing methods like coexpression analysis and graphical models have limitations in addressing diverse tasks comprehensively.

Purpose of the Study:

To present a novel deep learning framework for analyzing gene expression data.
To demonstrate the framework's ability to perform diverse gene-gene relationship mining tasks effectively.
To provide insights into the biological basis of the model's predictions.

Main Methods:

Developed a framework using an encoding for gene expression data followed by deep neural networks analysis.
Implemented a convolutional neural network for coexpression (CNNC) model.
Evaluated CNNC on tasks including transcription factor target prediction, disease-related gene identification, and causality inference.

Main Results:

CNNC significantly improves performance across various gene-gene relationship mining tasks compared to prior methods.
The CNNC encoding offers interpretable insights into biological mechanisms.
The framework demonstrates flexibility for integrating additional genomics data types.

Conclusions:

CNNC provides a powerful and versatile deep learning approach for gene-gene relationship inference from expression data.
The method enhances the prediction of gene functions, disease associations, and regulatory interactions.
Future extensions integrating multi-omics data promise further performance gains.