Getting started with TPOT for automated machine learning
A great post from Dr. Trang Le on how to get started with automated machine learning (AutoML) with our Tree-Based Pipeline Optimization Tool (TPOT) in Python.
From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)
A great post from Dr. Trang Le on how to get started with automated machine learning (AutoML) with our Tree-Based Pipeline Optimization Tool (TPOT) in Python.
We have expanded our TPOT automated machine learning method (AutoML) to metabolomics data.
New collaborative paper in Genetic Epidemiology with Dr. Yong Chen
Le TT, Fu W, Moore JH. Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics, in press (2019). [PubMed] [Bioinformatics]
Manduchi E, Hemerich D, van Setten J, Tragante V, Harakalova M, Pei J, Williams SM, van der Harst P, Asselbergs FW, Moore JH. A comparison of two workflows for regulome and transcriptome-based prioritization of genetic variants associated with myocardial mass. Genet Epidemiol. 2019 Sep;43(6):717-726. [PubMed] [Genetic Epi]
We released our open-source PennAI software for automated machine learning this week. Here is the Penn Medicine press release. Here is the Github link to the source code. More info can be found at the PennAI website. We think this will bring machine learning technology to novice users.
This was a fun proof-of-principle paper we did on using genetic programming to discover test statistics. We showed that with general principles that we could re-discover the two-sample t-test. This opens the door to the discovery of new test statistics for unsolved problems.
Our new paper with Dr. Kristel van Steen on approaches for improving evidence for statistical interactions.