Deep Learning for Single Cell Analysis

Deep Learning for Single Cell Analysis

2018

Bob Ziyang Ding (Math/Stats) and Daniel Chaofan Tao (ECE) spent ten weeks understanding how deep learning techniques can shed light on single cell analysis. Working with a large set of single-cell sequencing data, the team built an autoencoder pipeline and a device that will allow biologists to interactively visualize their own data.

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Disciplines Involved: Biology, Biomedical Engineering, PreHealth/PreMed, Biostatistics, all Quantitative STEM

Project Lead: Cliburn Chan

Project Manger: Kuei Yueh Ko

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Project Manager, Deep Learning for Single Cell Analysis