DeepPPI specifications


Unique identifier OMICS_18602
Name DeepPPI
Alternative name Deep neural networks for Protein-Protein Interactions
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
numpy, scipy, HDF5, h5py, scikit-learn, theano, keras
Maintained No




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Publication for Deep neural networks for Protein-Protein Interactions

DeepPPI citations


Opportunities and obstacles for deep learning in biology and medicine

PMCID: 5938574
PMID: 29618526
DOI: 10.1098/rsif.2017.0387

[…] rom which other approaches suffer.One of the key difficulties in applying deep learning techniques to binding prediction is the task of representing peptide and protein sequences in a meaningful way. DeepPPI [] made PPI predictions from a set of sequence and composition protein descriptors using a two-stage deep neural network that trained two subnetworks for each protein and combined them into a […]


Protein Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

Int J Mol Sci
PMCID: 5713342
PMID: 29117139
DOI: 10.3390/ijms18112373

[…] irs), and Mus musculus (313 interacting pairs) [], are used as independent test datasets to assess the generalization ability of DNN-LCTD. These datasets are available at […]

DeepPPI institution(s)
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China; Center of Information Support & Assurance Technology, Hefei, China
DeepPPI funding source(s)
Supported by grants from the National Science Foundation of China (61203290 and 61673020), the Outstanding Young Backbone Teachers Training (02303301), Provincial Natural Science Research Program of Higher Education Institutions of Anhui province (KJ2016A016) and Anhui Provincial Natural Science Foundation (1708085QF143).

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