Research

There is an abundance of publicly available data about various biological systems, but it can be difficult to draw insight from individual datasets. Our lab develops algorithms that integrate these data to help model and understand complex biological systems. Doing this allows us to investigate many different biological conditions, including those with limited data, such as rare diseases. We recognize that our lab won’t have all the answers, or even all of the questions, so we aim to develop tools and processes that any biologist can reuse. Our approach to research prioritizes transparency, rigor, and reproducibility.

The citations on this page were generated automatically from just identifiers using the Manubot cite utility developed right here in the Greene Lab!

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2020

Specific histone modifications associate with alternative exon selection during mammalian development
Specific histone modifications associate with alternative exon selection during mammalian development
Qiwen Hu, Casey S Greene, Elizabeth A Heller
Nucleic Acids Research  ·  22 Apr 2020  ·  doi:10.1093/nar/gkaa248

2019

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease
MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease
Jaclyn N. Taroni, Peter C. Grayson, Qiwen Hu, Sean Eddy, Matthias Kretzler, Peter A. Merkel, Casey S. Greene
Cell Systems  ·  01 May 2019  ·  doi:10.1016/j.cels.2019.04.003

2018

Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics
Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics
Qiwen Hu, Casey S. Greene
Biocomputing 2019  ·  01 Nov 2018  ·  doi:10.1142/9789813279827_0033

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