atBioNet specifications


Unique identifier OMICS_18851
Name atBioNet
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java
Computer skills Advanced
Version 1.1.10
Stability No
Maintained No



Add your version



This tool is not available anymore.

Publication for atBioNet

atBioNet in publications

PMCID: 5704338
PMID: 29201163
DOI: 10.3892/etm.2017.5185

[…] interactions and known disease-causing genes. endometriosis-associated genes were extracted from genotator and disgenet and biomarker network and pathway analyses were constructed using atbionet. of 100 input genes, 96 were strongly mapped to six major modules. the majority of the pathways in the first module were associated with the proliferation of cancer cells, the enriched […]

PMCID: 5463402
PMID: 28592245
DOI: 10.1186/s12920-017-0284-z

[…] analysis r packages for background correction and normalization of gene expression data. benjamini-hochberg false discovery rate algorithm was used to correct for multiple testing in geo2r []., atbionet identifies statistically significant functional modules using a fast network-clustering algorithm called structural clustering algorithm for networks (scan). atbionet interface is connected […]

PMCID: 5012434
PMID: 27625573
DOI: 10.4137/CIN.S39458

[…] a biomarker from urinary for recurrence in bladder cancer, and identifying noninvasive blood-based diagnosis markers for pancreatic cancer. ding et al. developed a web-based ppi network tool named atbionet, which was created by integrating seven public ppi databases for biomarker discovery using a fast network-clustering algorithm called structural clustering algorithm for networks, to find […]

PMCID: 4351319
PMID: 25750554
DOI: 10.3904/kjim.2015.30.2.148

[…] candidate genes such as socs3, pdgrfa, nfkbia, and ncf2, consistent with previous reports [,,,]. however, much follow up is necessary to validate these findings. in another study using the atbionet database system, two major modules were generated from seed genes using a fast-network-clustering algorithm: inflammatory process and immune activity []. the major signaling pathway involved […]

To access a full list of publications, you will need to upgrade to our premium service.

atBioNet institution(s)
ICF International at FDA's National Center for Toxicological Research, Jefferson, AR, USA; Divisions of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA; Department of Lymphoma and Myeloma, University of Texas MD Anderson Cancer Center, Houston, TX, USA; State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China; Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA
atBioNet funding source(s)
Supported by the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration (FDA) for postdoctoral and faculty support through the Oak Ridge Institute for Science and Education (ORISE).

atBioNet reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review atBioNet