Machine learning for synergistic network pharmacology: a comprehensive overview
Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …
understand drug actions and interactions with multiple targets. Network pharmacology has …
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
M Su, T Pan, QZ Chen, WW Zhou, Y Gong, G Xu… - Military Medical …, 2022 - Springer
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has
advanced our understanding of the pathogenesis of disease and provided valuable insights …
advanced our understanding of the pathogenesis of disease and provided valuable insights …
Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model
The gene regulatory structure of cells involves not only the regulatory relationship between
two genes, but also the cooperative associations of multiple genes. However, most gene …
two genes, but also the cooperative associations of multiple genes. However, most gene …
STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data
J Xu, A Zhang, F Liu, X Zhang - Bioinformatics, 2023 - academic.oup.com
Motivation Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to
infer cell-specific gene regulatory networks (GRNs), which is an important challenge in …
infer cell-specific gene regulatory networks (GRNs), which is an important challenge in …
Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data
G Chen, ZP Liu - Bioinformatics, 2022 - academic.oup.com
Motivation Single-cell RNA sequencing (scRNA-seq) data provides unprecedented
opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution …
opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution …
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data
Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to
provide an insight into a number of cellular phenotypes from the genomic level and reveal …
provide an insight into a number of cellular phenotypes from the genomic level and reveal …
[HTML][HTML] GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks
We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based
causal implicit generative model for simulating single-cell RNA-seq data, in silico …
causal implicit generative model for simulating single-cell RNA-seq data, in silico …
[HTML][HTML] Network-based approaches for modeling disease regulation and progression
Molecular interaction networks lay the foundation for studying how biological functions are
controlled by the complex interplay of genes and proteins. Investigating perturbed processes …
controlled by the complex interplay of genes and proteins. Investigating perturbed processes …
LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data
Motivation From a systematic perspective, it is crucial to infer and analyze gene regulatory
network (GRN) from high-throughput single-cell RNA sequencing data. However, most …
network (GRN) from high-throughput single-cell RNA sequencing data. However, most …
Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for
studying gene expression patterns at the single-cell level. Inferring gene regulatory networks …
studying gene expression patterns at the single-cell level. Inferring gene regulatory networks …