Hello and welcome! I’m Trang Le. I’m a postdoctoral fellow with Jason Moore at the Computational Genetics Lab, University of Pennsylvania. I enjoy developing machine learning methods for analyses of biomedical data, including neuroimage (functional/structural MRI), transcriptomics and genotypes. Most of the datasets I work with are high dimensional (i.e., have many predictors/features), so I spend most of my time building feature selection algorithms for these data. I trade my bias toward the nearest-neighbor concept for lower variance of my methods and better generalizability. When I’m not knee deep in data, I run, dance and seasonally ski.

Explorations

The Quaker Strong challenge

November 27 2019

In the last Quaker Strong challenge in the spring (March Madness edition), I was competing with several friends and enjoyed seeing how they were doing with the challenge. However, this time, with 46 registered participants, I figured it might be fun to write a few lines of code and make some fun visualization out of the logged progress. Code and details can be found here. To protect the …

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TPOT: Where do I start?

November 5 2019

Tree-based Pipeline Optimization Tool (TPOT) is an automated machine learning tool that helps the data scientist find the optimal model pipeline for their prediction problem. Using genetic programming (GP), TPOT explores different pipelines (sequences of feature selectors, model classifiers, etc.) and recommends one with optimal cross-validated score after a specified number of generations. Here …

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Last week, I got to attend a series of presentations followed by a panel discussion on open science at Penn Van Pelt-Dietrich library during the Open Access week. The panel featured (from left to right) Ted Satterthwaite, Jennifer Sisto, Daniel Himmelstein – all initiated enlightening discussions around different aspects of open science. ⊕ Photo credit: Rebecca Miller Jennifer Stiso …

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To age or not to age

October 1 2019

I recently stumbled upon this article by Gervasio Piñeiroa and colleages analyzing the method of model evaluation via plotting observed and predicted \(y\). The authors argue that, in plotting predicted or observed values, observed should be place on the \(y\)-axis vs. predicted on the \(x\)-axis. Because this article is unfortunately behind paywall, I’m going to show the quick simulation I have …

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Recent Works

  • Machine learning workshop, R Ladies Philly, Dec 2, 2019      
  • Multilocus risk scores, Penn Genetics Retreat, Sep 4, 2019      
  • npdr: Select features with nearest-neighbor concepts (2019)      
  • Trang T Le, Weixuan Fu and Jason H Moore (2019) Scaling tree-based automated machine learning to biomedical big data with a feature set selector. doi:10.1093/bioinformatics/btz470
  • TPOT: Overview and live demonstration, Clinical Research Informatics Core, University of Pennsylvania, Mar 13, 2019      
  • Trang T Lê, Zach Osman, D K Watson, Martin Dunn and B A McKinney (2019) Generalization of the Fermi pseudopotential. doi:10.1088/1402-4896/ab0811
  • STIR feature selection, Pacific Symposium on Biocomputing, Jan 5, 2019      
  • Trang T Le, Weixuan Fu and Jason H Moore (2018, preprint) Scaling tree-based automated machine learning to biomedical big data with a dataset selector. doi:10.1101/502484
  • Scalable automated machine learning, AI Therapeutics, Dec 21, 2018      
  • Statistical Inference Relief (STIR) feature selection, Mid-Atlantic Bioinformatics Conference, Oct 29, 2018