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. 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. PennAI comes with an initial knowledgebase generated from the PMLB benchmark suite.
Browse the repo:
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 in Bioinformatics and arxiv. Here's the biblatex:
@article{pennai_2020,
title = {Evaluating recommender systems for {AI}-driven biomedical informatics},
url = {https://doi.org/10.1093/bioinformatics/btaa698},
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.},
eprinttype = {arxiv},
eprint = {1905.09205},
keywords = {Computer Science - Machine Learning, Computer Science - Information Retrieval},
}
You can also find our original position paper on arxiv.