ArticleBriefings in bioinformatics2021
Deep learning of gene relationships from single cell time-course expression data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed.
- A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.Nature biomedical engineering · 2025Article
- Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.The Plant cell · 2025Article
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.Briefings in bioinformatics · 2025Article
- AttentionGRN: a functional and directed graph transformer for gene regulatory network reconstruction from scRNA-seq data.Briefings in bioinformatics · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer.BMC bioinformatics · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
- Integrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.Interdisciplinary sciences, computational life sciences · 2024Article
- CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.ACS synthetic biology · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.Briefings in bioinformatics · 2024Article
- Inference of gene regulatory networks based on directed graph convolutional networks.Briefings in bioinformatics · 2024Article
- TFvelo: gene regulation inspired RNA velocity estimation.Nature communications · 2024Article
- GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.Briefings in bioinformatics · 2024Article
- STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2023Article
- Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference.PNAS nexus · 2023Article
- Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG.Genome biology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offer several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, the data also raise new computational challenges. Using a novel encoding for scRNA-Seq expression data, we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as 3D tensor and train convolutional and recurrent neural networks for predicting interactions. We tested our time-course deep learning (TDL) models on five different time-series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time-series scRNA-Seq data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.