ArticleNPJ systems biology and applications2021
Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Mapping cancer heterogeneity: a consensus network approach to subtypes and pathways.Briefings in bioinformatics · 2025Article
- Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.Briefings in bioinformatics · 2024Article
- Quantitative estimates of the regulatory influence of long non-coding RNAs on global gene expression variation using TCGA breast cancer transcriptomic data.PLoS computational biology · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.Nature communications · 2024Article
- SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.Bioinformatics advances · 2024Article
- SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction.bioRxiv : the preprint server for biology · 2023Article
- Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer.NPJ systems biology and applications · 2023Article
- Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2.Scientific reports · 2021Article
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Abstract
Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.
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