.png){kind=icon}
返回
MAGIC: A Multimodal Adaptive GRN Inference Constructor
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jcim.5c02370.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Gene regulatory network (GRN) provides critical insights into the molecular mechanisms that govern cellular processes and disease pathogenesis, facilitating the identification of key regulatory factors and the discovery of potential therapeutic targets. Although numerous methods have been proposed to infer GRN from single-cell RNA sequencing (scRNA-seq) data, GRN inference remains challenging due to the inherent sparsity of scRNA-seq data and the naturally sparse connectivity of GRN. To address these challenges, this study proposes the Multimodal Adaptive GRN Inference Constructor (MAGIC), a method that improves GRN inference by aligning and integrating gene expression data, sequence information, and semantic features. Specifically, gene expression features reflect gene activity within cells, gene sequence features offer structural insights at the DNA level, and gene semantic features encapsulate functional meaning by leveraging biological knowledge bases. Furthermore, a consensus similarity network is constructed from multimodal gene similarity networks and integrated with known GRN to form a dual-topology network. To address the issue of sparse connectivity in GRN, a shared graph attention weight alignment module is employed. Following this, a Knowledge-Aware Multimodal Fusion Module is introduced to effectively integrate multimodal features by leveraging prior knowledge, thereby alleviating the inherent sparsity of scRNA-seq data. Finally, the fused features are used to infer GRNs. MAGIC achieved an average AUROC of 0.839 across seven scRNA-seq data sets using four types of ground-truth networks, outperforming other state-of-the-art models. Further analysis of two spatial transcriptomic data sets, bladder and breast cancer, demonstrates the robustness of MAGIC and its ability to uncover potential associations between transcription factors (TFs) and their target genes. MAGIC is publicly available at https://github.com/ydkvictory/MAGIC.
Keyword:
GENE REGULATORY NETWORKS
EXPRESSION
DATABASE
期刊
IF:
5.3
论文数:
9.1K
被引数:
4.0W
机构
引用论文
Viral carcinogenesis: revelation of molecular mechanisms and etiology of human disease.png)
CARCINOGENESIS
OAAI
CARCINOGENESISIF2.9
OAAI分享收藏The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells单细胞的假时间顺序揭示了细胞命运决定的动力学和调节剂NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C datascHiClassifier: 一种通过融合单细胞Hi-C数据中的多个特征集进行细胞类型预测的深度学习框架
OAAI
OAAI分享收藏Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing dataOAAI
OAAI分享收藏DHHNN: A Dynamic Hypergraph Hyperbolic Neural Network based on variational autoencoder for multimodal data integration and node classificationDHHNN: 基于变分自动编码器的动态超图双曲神经网络，用于多模态数据集成和节点分类INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction语言嵌入与生物特征的对比学习，用于跨模态编码和效应器预测NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Downregulation of Steroid Receptor Coactivator-2 Modulates Estrogen-Responsive Genes and Stimulates Proliferation of MCF-7 Breast Cancer CellsPLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networksNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏
查看更多内容