https://github.com/zietzm/zietzm

https://github.com/zietzm/zietzm

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/zietzm/zietzm
• Owner: zietzm
• Created: 2024-04-20T22:50:52.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-10-31T21:34:00.000Z (2 months ago)
• Last Synced: 2024-10-31T22:25:35.139Z (2 months ago)
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Introduction 👋

Hi there! I'm **Michael Zietz**. I'm a data scientist and genetics researcher, focused on developing new statistical and machine learnings methods with applications in healthcare and biomedicine. 🧬 I'm currently a Research Data Scientist at [Cedars-Sinai Computational Biomedicine](https://www.cedars-sinai.edu/research-education/research/departments-institutes/computational-biomedicine.html). Previously, I did my PhD at [Columbia University DBMI](https://www.dbmi.columbia.edu/) and studied Physics at [Penn](https://www.upenn.edu/), where I did research on heterogeneous networks in the [Greene Lab](https://greenelab.com/). 🕸️ I'm interested in methods development, reproducible research, and accelerating the pace of scientific progress on complex diseases. 🏥

## 💡 Some projects

#### [**WebGWAS**](https://webgwas.org): Instant, free, online genome-wide association studies (GWAS)

- [**Novel statistical method**](https://github.com/tatonetti-lab/indirect-gwas): Indirect GWAS gives an efficient approximation ([code](https://github.com/tatonetti-lab/indirect-gwas), [analysis](https://github.com/tatonetti-lab/indirect-gwas-analysis), [preprint](https://doi.org/10.1101/2023.11.20.567948))

- [**Backend web server**](https://github.com/tatonetti-lab/webgwas-backend): Computes GWAS in ~3 seconds, Rust Axum backend ([code](https://github.com/tatonetti-lab/webgwas-backend))

- [**Frontend application**](https://github.com/tatonetti-lab/webgwas-frontend): Interactive cohort builders and data visualization, React, Typescript ([code](https://github.com/tatonetti-lab/webgwas-frontend))

#### [**MaxGCP**](https://github.com/tatonetti-lab/maxgcp): Optimal phenotyping for research in complex disease genetics ([code](https://github.com/tatonetti-lab/maxgcp), [analysis](https://github.com/tatonetti-lab/maxgcp-analysis))

#### [**COVID Blood type**](https://doi.org/10.1038/s41467-020-19623-x): Study of the relationship between ABO type and COVID-19 ([analysis](https://github.com/zietzm/abo_covid_analysis), [paper](https://doi.org/10.1038/s41467-020-19623-x), [New York Times](https://www.nytimes.com/2020/07/15/science/coronavirus-blood-type.html))

#### [**XSwap**](https://github.com/hetio/xswap): A fast implementation of degree-preserving network randomization ([code](https://github.com/hetio/xswap), [analysis](https://github.com/greenelab/xswap-analysis), [paper](https://doi.org/10.1093/gigascience/giae001))

## Other tools 🔨

- [**sumher_rs**](https://github.com/zietzm/sumher_rs): Efficiently estimating a genetic covariance matrix using SumHer ([code](https://github.com/zietzm/sumher_rs))

- [**mdav**](https://github.com/zietzm/mdav): Data anonymization tool implementing maximum distance to the average vector (MDAV) anonymization ([code](https://github.com/zietzm/mdav))

- [**pymbend**](https://github.com/zietzm/pymbend): Bending matrices to be positive semi-definite ([code](https://github.com/zietzm/pymbend))

- [**Micromanubot**](https://github.com/zietzm/micromanubot): A user-friendly build tool for academic preprints in LaTeX ([code](https://github.com/zietzm/micromanubot))

- [**OnsidesDB.org**](https://onsidesdb.org): Interactive exploration of adverse drug events extracted using large language models ([code](https://github.com/tatonetti-lab/onsides-web))