Visual Machine Learning for Next-Generation GWAS
GenAMap
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Data Management

GenAMap organizes data in cloud-based projects for easy data handling.

Algorithm Automation

GenAMap uses parallelization for easy scaling to human-scale data.

Interactive Visualizations

GenAMap makes it easy to explore large association studies.

Features

Structured Assocation Mapping

Hypothesis Testing

Confounding effect correction

Cloud-based projects

Integration with NIH NCI GDC

Visualizations

Our Team

Eric P. Xing

Principal Investigator

Min Kyung Lee

Visualization Lead

Haohan Wang

Machine Learning Lead

Benjamin J. Lengerich

Machine Learning Lead

Team Members: Joey Gibli, Clarence Ngoh, Danielle Hu, Jingkang Yang, Hilary Lai.

Former Members: Ross Curtis, Dylan Steele, Bryan Yan, Aditya Gautam, Liuyu Jin, Beilin Li.