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Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

Holly Steach, Siddharth Viswanath, Yixuan He

Biorxiv : the Preprint Server for Biology

|December 18, 2023

Summary

This summary is machine-generated.

Researchers developed GEFMAP, a deep learning method to predict cellular metabolism from gene expression data. This tool helps understand cell function by analyzing metabolic pathways at single-cell resolution.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Systems Biology
Computational Biology
Metabolomics

Background:

Single-cell RNA sequencing (scRNAseq) provides gene expression insights but lacks metabolic data.
Metabolic state is crucial for cellular processes, yet single-cell metabolomics is challenging.
Multimodal regulation highlights the need to integrate gene expression with metabolic function.

Conclusions:

GEFMAP offers a novel way to infer cellular metabolism from gene expression.
This method advances the integration of multiomics data for biological discovery.
It provides a scalable solution for understanding cellular metabolic states.