DeepRank
Scoring 3D protein-protein interaction models using deep learning
iScore
A framework and predictor based on support vector machine and random walk graph kernel for scoring protein-protein interfaces.
3
mentions
4
contributors
Cite this software
Choose a version:
- [[release.tag]]
DOI:
[[ releases.length > 0 ? releases[selectedIndex].doi : conceptDOI ]]
Choose a reference manager file format:
- [[ format.displayName ]]
What iScore can do for you
- Provides a state-of-the-art scoring predictor trained on the data of Docking Benchmark 5 (https://zlab.umassmed.edu/benchmark/)
- Provides easy-to-use interface to train a new scoring predictor
- Easily extended with more different and various features of protein interface
- Active and competitive player in the CAPRI challenge for 3D structure predictions of protein complexes (http://www.capri-docking.org)
iScore offers simple solutions to classify protein-protein interfaces using a support vector machine approach on graph kernels. The simplest way to use iScore is through dedicated binaries that hide the complexity of the approach and allows access to the code with simple command line interfaces. The two binaries are iscore.train and iscore.predict that respectively train a model using a trainging set and use this model to predict the near-native character of unkown conformations.
Tags
Programming Language
- Python
License
- Apache-2.0
Source code
Participating organizations
Mentions
Projects with iScore
Contributors
-
Cunliang GengNetherlands eScience Center
-
Nicolas RenaudNetherlands eScience Center
-
Yong Jung
-
Li XueUtrecht University
Contact person
Information for page maintainers
OAI-PMH metadata:
OK
citation metadata:
OK