Ensemble Learning Approach for Stability Prediction of Interface and Core mutations (ELASPIC).
Project description
# ELASPIC
[![anaconda](https://anaconda.org/kimlab/elaspic/badges/version.svg?style=flat-square)](https://anaconda.org/kimlab/elaspic) [![docs](https://img.shields.io/badge/docs-latest-blue.svg?style=flat-square&?version=latest)](http://kimlaborg.github.io/elaspic) [![travis](https://img.shields.io/travis/kimlaborg/elaspic.svg?style=flat-square)](https://travis-ci.org/kimlaborg/elaspic) [![codecov](https://img.shields.io/codecov/c/github/kimlaborb/elaspic.svg?style=flat-square)](https://codecov.io/gh/kimlaborg/elaspic)
## Introduction
Welcome to the ELASPIC code repository!
Complete documentation is availible on [ReadTheDocs](http://elaspic.readthedocs.io).
For a small number of mutations, you can try running ELASPIC using our [webserver](http://elaspic.kimlab.org).
## References
Witvliet D, Strokach A, Giraldo-Forero AF, Teyra J, Colak R, and Kim PM (2016) ELASPIC web-server: proteome-wide structure based prediction of mutation effects on protein stability and binding affinity. Bioinformatics (2016) 32 (10): 1589-1591. doi: [10.1093/bioinformatics/btw031](https://doi.org/10.1093/bioinformatics/btw031).
Berliner N, Teyra J, Çolak R, Garcia Lopez S, Kim PM (2014) Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation. PLoS ONE 9(9): e107353. doi: [10.1371/journal.pone.0107353](https://doi.org/10.1371/journal.pone.0107353).
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