ArticlePLoS computational biology2019
Multi-study inference of regulatory networks for more accurate models of gene regulation.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization.bioRxiv : the preprint server for biology · 2026Article
- Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.Briefings in bioinformatics · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.International journal of molecular sciences · 2024Article
- Flexible modeling of regulatory networks improves transcription factor activity estimation.NPJ systems biology and applications · 2024Article
- DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.Briefings in bioinformatics · 2024Article
- Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.Genome biology · 2024Article
- iLSGRN: inference of large-scale gene regulatory networks based on multi-model fusion.Bioinformatics (Oxford, England) · 2023Article
- MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework.Bioinformatics (Oxford, England) · 2023Article
- Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.Nature communications · 2023Article
- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Coordinated control of neuronal differentiation and wiring by sustained transcription factors.Science (New York, N.Y.) · 2022Article
- RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.BMC bioinformatics · 2022Article
- High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.Bioinformatics (Oxford, England) · 2022Article
- A 1p/19q Codeletion-Associated Immune Signature for Predicting Lower Grade Glioma Prognosis.Cellular and molecular neurobiology · 2022Article
- Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box.NPJ systems biology and applications · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- The Promise of Patient-Derived Colon Organoids to Model Ulcerative Colitis.Inflammatory bowel diseases · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.
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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 Socratic graph.