LR_PPI statistics

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Citations per year

Number of citations per year for the bioinformatics software tool LR_PPI

Tool usage distribution map

This map represents all the scientific publications referring to LR_PPI per scientific context
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chevron_left Protein-protein interaction prediction chevron_right
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LR_PPI specifications


Unique identifier OMICS_18479
Alternative name Latent dirichlet allocation-Random forest Protein-Protein Interaction
Interface Web user interface
Restrictions to use Academic or non-commercial use
Input data Some protein sequences.
Computer skills Basic
Stability Stable
Maintained Yes



  • person_outline Hong-Bin Shen

Publication for Latent dirichlet allocation-Random forest Protein-Protein Interaction

LR_PPI citation


Sequence based prediction of protein protein interaction using a deep learning algorithm

BMC Bioinformatics
PMCID: 5445391
PMID: 28545462
DOI: 10.1186/s12859-017-1700-2

[…] e compared prediction abilities of the two models on external test sets. We submitted the 2010 HPRD, the 2010 HPRD NR, and the DIP datasets to Pan’s online server (, and the returned prediction accuracies on these datasets were 89.15%, 86.70%, and 90.04%, respectively. These values were lower than those obtained with our model (99.21, 97.14 and 93.77%, re […]

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LR_PPI institution(s)
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai, China
LR_PPI funding source(s)
Supported by the National Natural Science Foundation of China (Grant No. 60704047), Science and Technology Commission of Shanghai Municipality (Grant No. 08ZR1410600, 08JC1410600), sponsored by Shanghai Pujiang Program and Innovation Program of Shanghai Municipal Education Commission (10ZZ17).

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