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| [](https://github.com/EpistasisLab/Aliro/blob/master/LICENSE) [](https://github.com/EpistasisLab/Aliro/actions/workflows/aliro_tests.yml) [](https://coveralls.io/github/EpistasisLab/pennai) | ||
| [](https://github.com/epistasislab/aliro/blob/master/license) [](https://github.com/epistasislab/aliro/actions/workflows/aliro_tests.yml) [](https://coveralls.io/github/epistasislab/pennai) | ||
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| Aliro: AI-Driven Data Science | ||
| aliro: ai-driven data science | ||
| ================================== | ||
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| **Aliro** is an easy-to-use data science assistant. | ||
| It allows researchers without machine learning or coding expertise to run supervised machine learning analysis through a clean web interface. | ||
| It provides results visualization and reproducible scripts so that the analysis can be taken anywhere. | ||
| And, it has an *AI* assistant that can choose the analysis to run for you. Dataset profiles are generated and added to a knowledgebase as experiments are run, and the AI assistant learns from this to give more informed recommendations as it is used. Aliro comes with an initial knowledgebase generated from the [PMLB benchmark suite](https://github.com/EpistasisLab/penn-ml-benchmarks). | ||
| **aliro** is an easy-to-use data science assistant. | ||
| it allows researchers without machine learning or coding expertise to run supervised machine learning analysis through a clean web interface. | ||
| it provides results visualization and reproducible scripts so that the analysis can be taken anywhere. | ||
| and, it has an *ai* assistant that can choose the analysis to run for you. dataset profiles are generated and added to a knowledgebase as experiments are run, and the ai assistant learns from this to give more informed recommendations as it is used. aliro comes with an initial knowledgebase generated from the [pmlb benchmark suite](https://github.com/epistasislab/penn-ml-benchmarks). | ||
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| [**Documentation**](https://epistasislab.github.io/Aliro/) | ||
| [**documentation**](https://epistasislab.github.io/aliro/) | ||
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| [**Latest Production Release**](https://github.com/EpistasisLab/Aliro/releases/latest) | ||
| [**latest production release**](https://github.com/epistasislab/aliro/releases/latest) | ||
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| Browse the repo: | ||
| - [User Guide](./docs/guides/userGuide.md) | ||
| - [Developer Guide](./docs/guides/developerGuide.md) | ||
| browse the repo: | ||
| - [user guide](./docs/guides/userguide.md) | ||
| - [developer guide](./docs/guides/developerguide.md) | ||
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| About the Project | ||
| about the project | ||
| ================= | ||
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| Aliro is actively developed by the [Institute for Biomedical Informatics](http://upibi.org) at the University of Pennsylvania. | ||
| Contributors include Heather Williams, Weixuan Fu, William La Cava, Josh Cohen, | ||
| Steve Vitale, Sharon Tartarone, Randal Olson, Patryk Orzechowski, and Jason Moore. | ||
| aliro is actively developed by the Center for Artificial Intelligence Research (CAIR) in the [Department of Computational Biomedicine](https://www.cedars-sinai.edu/research/departments-institutes/computational-biomedicine.html) at [Cedars-Sinai Medical Center](https://www.cedars-sinai.org/) in Los Angeles. | ||
| Contributors include Hyunjun Choi, Miguel Hernandez, Nick Matsumoto, Jay Moran, Paul Wang, and Jason Moore (PI). | ||
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| Cite | ||
| cite | ||
| ==== | ||
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| An up-to-date paper describing AI methodology is available in [Bioinformatics](https://doi.org/10.1093/bioinformatics/btaa698) and [arxiv](http://arxiv.org/abs/1905.09205). | ||
| Here's the biblatex: | ||
| an up-to-date paper describing ai methodology is available in [bioinformatics](https://doi.org/10.1093/bioinformatics/btaa698) and [arxiv](http://arxiv.org/abs/1905.09205). | ||
| here's the biblatex: | ||
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| ``` | ||
| @article{pennai_2020, | ||
| title = {Evaluating recommender systems for {AI}-driven biomedical informatics}, | ||
| title = {evaluating recommender systems for {ai}-driven biomedical informatics}, | ||
| url = {https://doi.org/10.1093/bioinformatics/btaa698}, | ||
| journaltitle = {Bioinformatics}, | ||
| journaltitle = {bioinformatics}, | ||
| doi = {10.1093/bioinformatics/btaa698}, | ||
| year = {2020}, | ||
| author = {La Cava, William and Williams, Heather and Fu, Weixuan and Vitale, Steve and Srivatsan, Durga and Moore, Jason H.}, | ||
| author = {la cava, william and williams, heather and fu, weixuan and vitale, steve and srivatsan, durga and moore, jason h.}, | ||
| eprinttype = {arxiv}, | ||
| eprint = {1905.09205}, | ||
| keywords = {Computer Science - Machine Learning, Computer Science - Information Retrieval}, | ||
| keywords = {computer science - machine learning, computer science - information retrieval}, | ||
| } | ||
| ``` | ||
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| You can also find our original position paper on [arxiv](https://arxiv.org/abs/1705.00594). | ||
| you can also find our original position paper on [arxiv](https://arxiv.org/abs/1705.00594). |