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deepNF specifications
Information
| Unique identifier | OMICS_24071 |
|---|---|
| Name | deepNF |
| Software type | Application/Script |
| Interface | Command line interface |
| Restrictions to use | None |
| Programming languages | Python |
| Computer skills | Advanced |
| Stability | Stable |
| Requirements |
python, scikit-learn, numpy, keras, tensorflow, ppmi, deepnf
|
| Maintained | Yes |
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Versioning
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Maintainers
- person_outline Vladimir Gligorijević
- person_outline Meet Barot
- person_outline Richard Bonneau
Publication for deepNF
2018 Bioinformatics
PMCID: 6223364
PMID: 29868758
DOI: 10.1093/bioinformatics/bty440
deepNF citation
library_books
Opportunities and obstacles for deep learning in biology and medicine
2018
PMCID: 5938574
PMID: 29618526
DOI: 10.1098/rsif.2017.0387
[…] uctural information.An important challenge in PPI network prediction is the task of combining different networks and types of networks. Gligorijevic et al. [] developed a multimodal deep autoencoder, deepNF, to find a feature representation common among several different PPI networks. This common lower-level representation allows for the combination of various PPI data sources towards a single pre […]
deepNF institution(s)
Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA; Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, NY, USA
deepNF funding source(s)
Supported by the Simons Foundation, the NIH, the NSF and NYU for supporting this research, particularly NSF: MCB-1158273, IOS-1339362, MCB-1412232, MCB- 1355462, IOS-0922738, MCB-0929338, and NIH: 2R01GM032877-25A1.
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