ArticleG3 (Bethesda, Md.)2015
Gene Network Reconstruction by Integration of Prior Biological Knowledge.
Article in G3 (Bethesda, Md.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.Nucleic acids research · 2026Article
- Development of a prognostic multiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first line immunotherapy.Scientific reports · 2025Article
- Bio-primed machine learning to enhance discovery of relevant biomarkers.NPJ precision oncology · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.PLoS computational biology · 2024Article
- Optimal entropic properties of SARS-CoV-2 RNA sequences.Royal Society open science · 2024Article
- Multi-omics regulatory network inference in the presence of missing data.Briefings in bioinformatics · 2023Article
- Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data.BMC genomics · 2023Article
- StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction.Bioinformatics advances · 2023Article
- Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge.Entropy (Basel, Switzerland) · 2022Article
- Network reconstruction for trans acting genetic loci using multi-omics data and prior information.Genome medicine · 2022Article
- Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks.Scientific reports · 2022Article
- Correlation Imputation for Single-Cell RNA-seq.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Tailored graphical lasso for data integration in gene network reconstruction.BMC bioinformatics · 2021Article
- An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning.Frontiers in genetics · 2021Article
- Determinants of correlated expression of transcription factors and their target genes.Nucleic acids research · 2020Article
- A strategy to incorporate prior knowledge into correlation network cutoff selection.Nature communications · 2020Article
- Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2020Article
- Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- Inferring Interaction Networks From Multi-Omics Data.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of high-throughput genomic technologies, large, genome-wide datasets have been collected, and the integration of these datasets should provide large-scale, multidimensional, and insightful views of biological systems. We developed a method for gene association network construction based on gene expression data that integrate a variety of biological resources. Assuming gene expression data are from a multivariate Gaussian distribution, a graphical lasso (glasso) algorithm is able to estimate the sparse inverse covariance matrix by a lasso (L1) penalty. The inverse covariance matrix can be seen as direct correlation between gene pairs in the gene association network. In our work, instead of using a single penalty, different penalty values were applied for gene pairs based on a priori knowledge as to whether the two genes should be connected. The a priori information can be calculated or retrieved from other biological data, e.g., Gene Ontology similarity, protein-protein interaction, gene regulatory network. By incorporating prior knowledge, the weighted graphical lasso (wglasso) outperforms the original glasso both on simulations and on data from Arabidopsis. Simulation studies show that even when some prior knowledge is not correct, the overall quality of the wglasso network was still greater than when not incorporating that information, e.g., glasso.
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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.