GuiltyTargets specifications
Information
| Unique identifier | OMICS_33793 |
|---|---|
| Name | GuiltyTargets |
| Software type | Application/Script |
| Interface | Command line interface |
| Restrictions to use | None |
| Input data | A protein-protein interaction network, an experiment file, a list of Entrez ids of known targets and a configuration file. |
| Operating system | Unix/Linux |
| Programming languages | Python |
| License | MIT License |
| Computer skills | Advanced |
| Stability | Stable |
| Requirements |
numpy, pandas, python, _igraph, scipy, scikit-learn, click, tqdm, easy-config, GAT2VEC
|
| Maintained | Yes |
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Maintainer
- person_outline Holger Fröhlich <>
Publication for GuiltyTargets
library_books
GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning.
2019 BioRxiv
DOI: 10.1101/521161
GuiltyTargets institution(s)
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany; UCB Biosciences GmbH, Monheim, Germany
GuiltyTargets funding source(s)
Supported by Fraunhofer-Gesellschaft.
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