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ToppNet specifications

Information


Unique identifier OMICS_27569
Name ToppNet
Interface Web user interface
Restrictions to use None
Input data Two gene lists (training and test).
Output format HTML
Computer skills Basic
Stability Stable
Maintained Yes

Maintainers


  • person_outline Bruce Aronow
  • person_outline Anil Jegga
  • person_outline Eric Bardes

Publication for ToppNet

ToppNet citations

 (10)
library_books

The cucurbitacins D, E, and I from Ecballium elaterium (L.) upregulate the LC3 gene and induce cell cycle arrest in human gastric cancer cell line AGS

2018
PMCID: 5817168
PMID: 29511491
DOI: 10.22038/ijbms.2018.25175.6236

[…] them in gastric cancer. for training gene set, we used the snps3d server (http://www.snps3d.org/) to get the list of candidate genes associated with gastric cancer. after that, prioritizing servers: toppnet (https://toppgene.cchmc.org/network.jsp), which prioritize and rank candidate genes based on topological features in protein-protein interaction network (), and endeavour […]

library_books

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD related networks

2017
Oncotarget
PMCID: 5732734
PMID: 29262568
DOI: 10.18632/oncotarget.21874

[…] analysis of the top 100 genes in these two networks suggested the correlation of copd and these genes. the performance of our gene prioritization method was superior to that of toppgene and toppnet for genes from the copd-related metabolic network or the copd-related ppi network after assessing using leave-one-out cross-validation, literature validation and functional enrichment […]

library_books

Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information

2017
PLoS One
PMCID: 5584748
PMID: 28873096
DOI: 10.1371/journal.pone.0184299

[…] between genes. then a gene prioritization method was applied to the copd-related metabolic network to prioritize copd candidate genes. the gene prioritization method was superior to toppgene and toppnet in both literature validation and functional enrichment analysis. top-ranked genes prioritized from the metabolic perspective with functional information could promote the better […]

library_books

Drug Repositioning for Alzheimer’s Disease Based on Systematic ‘omics’ Data Mining

2016
PLoS One
PMCID: 5179106
PMID: 28005991
DOI: 10.1371/journal.pone.0168812

[…] drug targets derived from our ‘omics’ mining method, we used the toppgene tool (https://toppgene.cchmc.org), which ranks candidate genes based on functional similarity to the training genes, and the toppnet tool (https://toppgene.cchmc.org), which ranks candidate genes based on topological features in protein-protein interaction networks and their similarity to the training genes []. […]

library_books

Transcriptional profile of SH SY5Y human neuroblastoma cells transfected by Toxoplasma rhoptry protein 16

2016
PMCID: 5101894
PMID: 27666388
DOI: 10.3892/mmr.2016.5758

[…] in . genes associated with neural system development, apoptosis and transcriptional regulation are listed in according to the functional categories., according to the instructions of toppgene and toppnet, the ‘training gene set’ contained 12 genes associated with toxoplasma, which were collected from the omim and genecards database searches (). prioritization of candidate genes was obtained […]

library_books

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism Related Genes

2016
PLoS One
PMCID: 4822849
PMID: 27050193
DOI: 10.1371/journal.pone.0153006

[…] of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. compared with two well-known gene prioritization tools endeavour and toppnet, fip was shown to have better performance. the approach provides a valuable alternative for drug targets discovery and disease therapy., all profile files are available from the geo database […]


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ToppNet institution(s)
Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
ToppNet funding source(s)
Supported by the State of Ohio Computational Medicine Center (ODD TECH 04-042); National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (NIH/NIDDK) 1U01 DK70219 (Murine Atlas of a Genitourinary Smooth Muscle Development); PHS Grant P30 DK078392 (Cincinnati Digestive Health Center).

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