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| * [[HumanSVMPredictions.zip|Human predictions]] * [[FlySVMPredictions.zip|Fly predictions]] * [[WormSVMPredictions.zip|Worm predictions]] |
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Proteome scanning of PDZ domain interactions using support vector machines
Contents
Background
An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will greatly increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.
Results
We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and compared to published state of art predictors, is more accurate and precise.
SVM Predictions
The following are SVM proteome scanning predictions for human, fly and worm PDZ domains. The format of the output is:
<peptide sequence> <decision value> <validation method> <transcript ids>
Supplementary
- Models
- Chen model parameter and binding site encoding files
- Stiffler model parameter files
- Proteomes
- Ensembl proteome files for Human, Worm and Fly
- Experiment Interaction files (in peptide file format)
- Fly files from Chen
- Human files from Sidhu
- Mouse files from Stiffler
- Worm files from Chen
- Curated Interaction files (flat files)
- PDZBase for Human (Worm and Fly included, but not used)
- Human Protein Reference Database
- Phage codon bias files
- Models
Source Code
- jfreechart 1.0.12 (and dependencies)
- weka 3.9.1
auc calculator (Davis & Goadrich, 2006)
BioJava 1.5
- iText 2.1.3
- jmatio
- BRAIN 1.0.5 (pdzsvm)
- libSVM 2.8.9 (pdzsvm)
Team
- Shirley Hui
- Gary Bader