ArticleMethods in molecular biology (Clifton, N.J.)2021
Co-expression Networks in Predicting Transcriptional Gene Regulation.
Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Article
- Transcription-based comparison ofMicrobiology spectrum · 2026Article
- The polygenic, omnigenic and stratagenic models of complex disease risk.Nature genetics · 2026Review
- Transcriptome Analysis of Sesame (International journal of molecular sciences · 2024Article
- iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.PLoS computational biology · 2023Article
- A Novel Role ofInternational journal of molecular sciences · 2023Article
- Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.
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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.