https://github.com/epistasislab/epistasislab.github.io
Identifying the complex genetic architectures of disease
https://github.com/epistasislab/epistasislab.github.io
Last synced: 10 months ago
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Identifying the complex genetic architectures of disease
- Host: GitHub
- URL: https://github.com/epistasislab/epistasislab.github.io
- Owner: EpistasisLab
- Created: 2017-02-16T15:27:12.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2021-11-11T23:31:33.000Z (about 4 years ago)
- Last Synced: 2025-01-17T09:26:58.288Z (12 months ago)
- Homepage: http://epistasislab.github.io
- Size: 8.79 KB
- Stars: 5
- Watchers: 5
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
This is the documentation page for the [Epistasis Lab](http://epistasis.org), a research group at at [Cedars-Sinai Medical Center](https://www.cedars-sinai.org/locations/cedars-sinai-main-campus-89.html) in Los Angeles, CA (USA).
In a nutshell, our lab develops computational methods to identify the complex genetic and environmental interactions that lead to human disease.
Our methods address [challenges](http://www.nature.com/nrg/journal/v11/n6/full/nrg2809.html) such as epistasis, heterogeneity, and scalability.
You can browse our projects below for more information.
Methods
===
[EVE](https://github.com/EpistasisLab/EVE): ENSEMBL VEP on EC2
[ellyn](https://epistasislab.github.io/ellyn): A sklearn-compatible linear genetic programming system for symbolic regression and classification.
[ExSTraCS](https://github.com/ryanurbs/ExSTraCS_2.0): Extended Supervised Tracking and Classifying System
[FEAT](https://lacava.github.io/feat): A feature engineering automation tool for regression and classification.
[FEW](https://lacava.github.io/few): A feature engineering wrapper for scikit-learn.
[scikit-mdr](https://github.com/EpistasisLab/scikit-mdr): A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.
[scikit-rebate](https://epistasislab.github.io/scikit-rebate/): A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
[stir](https://github.com/insilico/stir/): ReliefF on steroids
[tpot](https://epistasislab.github.io/tpot/): A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming.
[treeheatr](https://github.com/trang1618/treeheatr): Heatmap-integrated decision tree visualizations
Useful Collections
===
[ClinicalDataSources](https://github.com/EpistasisLab/ClinicalDataSources): Open or Easy Access Clinical Data Sources for Biomedical Research
[Penn ML Benchmarks (PMLB)](https://github.com/EpistasisLab/penn-ml-benchmarks): A large, curated repository of benchmarks for evaluating supervised machine learning algorithms.
[pmlbr](https://github.com/EpistasisLab/pmlbr): an R interface to PMLB