Summary of Gene Regulatory Network Inference From Pre-trained Single-cell Transcriptomics Transformer with Joint Graph Learning, by Sindhura Kommu et al.
Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning
by Sindhura Kommu, Yizhi Wang, Yue Wang, Xuan Wang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data by leveraging pre-trained single-cell BERT-based models and graph neural networks. The joint graph learning method integrates contextual representations learned from scRNA-seq data with structured biological knowledge from existing GRNs, allowing for effective reasoning over both gene expression levels and cellular regulatory mechanisms. The approach outperforms current state-of-the-art baselines on human cell benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses a special kind of AI model to help scientists understand how genes work together in cells. It takes single-cell RNA sequencing data, which shows what genes are turned on or off in each cell, and combines it with information about known gene interactions. This helps the model learn more accurate relationships between genes and create better models of cellular regulatory networks. The results show that this approach can accurately predict how genes work together in different types of cells. |
Keywords
* Artificial intelligence * Bert
