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InPrePPI specifications

Information


Unique identifier OMICS_11012
Name InPrePPI
Alternative name Integration method for Prediction of Protein-Protein Interactions
Interface Web user interface
Restrictions to use None
Input data Protein sequences
Input format FASTA
Programming languages C++, Java
License GNU General Public License version 3.0
Computer skills Basic
Stability No
Maintained No

Maintainer


This tool is not available anymore.

Information


Unique identifier OMICS_11012
Name InPrePPI
Alternative name Integration method for Prediction of Protein-Protein Interactions
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Protein sequences
Input format FASTA
Operating system Unix/Linux
Programming languages C++, Java
License GNU General Public License version 3.0
Computer skills Advanced
Stability No
Maintained No

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Publication for Integration method for Prediction of Protein-Protein Interactions

InPrePPI in publications

 (4)
PMCID: 5133940
PMID: 27879651
DOI: 10.3390/ijms17111946

[…] identifying the max lr of each pair-based evidence, and then integrating the above results with naive bayes algorithm and generating final composite likelihood ratio from multiplicative lr., case 2: inpreppi (an integrated evaluation method based on genomic context for predicting protein−protein interactions in prokaryotic genomes) [] uses ac value (an integrated value of the accuracy […]

PMCID: 4614683
PMID: 26490033
DOI: 10.1038/srep15519

[…] the same go term annotation should be more likely to interact with each other., the genome context methods (gene fusion, gene neighborhood, phylogenetic profile and gene cluster) were analyzed by inpreppi. domain data were obtained from domine database (database of protein domain interactions, http://domine.utdallas.edu/cgi-bin/domine) and pfam database (http://pfam.sanger.ac.uk/)., orthologs […]

PMCID: 4575426
PMID: 26384938
DOI: 10.1186/s12859-015-0741-7

[…] maximum number in common between the two msas is an important aspect on these methodologies as its performance is highly influenced by these two parameters, i.e. the diversity of alignment., we used inpreppi [], which implements all the genome context based methods (gf, pp, gn and go), which requires a reference genome dataset and genome annotation (e.g. operon units) as described above. among […]

PMCID: 3145649
PMID: 21829334
DOI: 10.1371/journal.pcbi.1002110

[…] information of ppis. bader et al. 2004 weighted their positive and negative training examples inversely according to their fraction of the training set to favor 0.5 as the prior dividing threshold. inpreppi used a naïve bayesian fashion to integrate multiple data sources by multiplying a weight, which is approximately estimated for each data source. however, the contributions of data sources […]


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InPrePPI institution(s)
Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; Bioinformation Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Graduate School, Chinese Academy of Sciences, Shanghai, China; School of Life Sciences and Technology, Shanghai Jiaotong University, Shanghai, China; Department of Human Genetics, Virginia Commonwealth University, Richmond, VA, USA; Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA

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