ArticleScientific reports2016
Differential network analysis from cross-platform gene expression data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 38 citations in OpenAlex.
- Integrative gene and isoform co-expression networks reveal regulatory rewiring in stress-related psychiatric disorders.iScience · 2025Article
- Interaction-based transcriptome analysis via differential network inference.Briefings in bioinformatics · 2022Article
- A novel twelve-gene signature to predict neoadjuvant chemotherapy response and prognosis in breast cancer.Frontiers in immunology · 2022Article
- Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge.Frontiers in genetics · 2021Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- Integrating gene regulatory pathways into differential network analysis of gene expression data.Scientific reports · 2019Article
- Article
- Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.Frontiers in genetics · 2019Review
- DiffGRN: differential gene regulatory network analysis.International journal of data mining and bioinformatics · 2018Article
- A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis.International journal of genomics · 2017Article
- Differential Network Analysis: A Statistical Perspective.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes.
Identifiers
What OpenQuestion holds
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.