PennAI 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.

Documentation

Latest Production Release

Browse the repo:

• User Guide
• Developer Guide

PennAI is actively developed by the Institute for Biomedical Informatics 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.

An up-to-date paper describing AI methodology is available on arxiv. Here's the biblatex:

@article{pennai_2019,
	title = {Evaluating recommender systems for {AI}-driven data science},
	url = {http://arxiv.org/abs/1905.09205},
	journaltitle = {{arXiv}:1905.09205 [cs]},
	author = {La Cava, William and Williams, Heather and Fu, Weixuan and Moore, Jason H.},
	urldate = {2019-05-30},
	date = {2019-05-22},
	eprinttype = {arxiv},
	eprint = {1905.09205},
	keywords = {Computer Science - Machine Learning, Computer Science - Information Retrieval},
}

You can also find our original position paper on arxiv.