ArticleCancer informatics2014
Assessment of subnetwork detection methods for breast cancer.
Article in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 10 citations in OpenAlex.
- SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma.BMC medical genomics · 2020Article
- Efficient identification of multiple pathways: RNA-Seq analysis of livers fromBMC bioinformatics · 2020Article
- GeneSurrounder: network-based identification of disease genes in expression data.BMC bioinformatics · 2019Article
- Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.Biology direct · 2018Article
- Network-based analysis of omics data: the LEAN method.Bioinformatics (Oxford, England) · 2017Article
- IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer.CPT: pharmacometrics & systems pharmacology · 2017Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.
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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.