ArticleJournal of proteome research2016
New Method for Joint Network Analysis Reveals Common and Different Coexpression Patterns among Genes and Proteins in Breast Cancer.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
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- Preclinical Serological Signatures are Associated With Complicated Crohn's Disease Phenotype at Diagnosis.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2023Article
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- Statistical and Computational Methods for Proteogenomic Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.Cell · 2020Article
- PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data.Proteomics · 2020Article
- ProNetView-ccRCC: A Web-Based Portal to Interactively Explore Clear Cell Renal Cell Carcinoma Proteogenomics Networks.Proteomics · 2020Article
- Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.Cell · 2020Article
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- Data-adaptive multi-locus association testing in subjects with arbitrary genealogical relationships.Statistical applications in genetics and molecular biology · 2019Article
- Differential Integration of Transcriptome and Proteome Identifies Pan-Cancer Prognostic Biomarkers.Frontiers in genetics · 2018Article
- A new method to study the change of miRNA-mRNA interactions due to environmental exposures.Bioinformatics (Oxford, England) · 2017Article
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
We focus on characterizing common and different coexpression patterns among RNAs and proteins in breast cancer tumors. To address this problem, we introduce Joint Random Forest (JRF), a novel nonparametric algorithm to simultaneously estimate multiple coexpression networks by effectively borrowing information across protein and gene expression data. The performance of JRF was evaluated through extensive simulation studies using different network topologies and data distribution functions. Advantages of JRF over other algorithms that estimate class-specific networks separately were observed across all simulation settings. JRF also outperformed a competing method based on Gaussian graphic models. We then applied JRF to simultaneously construct gene and protein coexpression networks based on protein and RNAseq data from CPTAC-TCGA breast cancer study. We identified interesting common and differential coexpression patterns among genes and proteins. This information can help to cast light on the potential disease mechanisms of breast cancer.
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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.