Michael Zietz

MaxGCP improves GWAS power by optimizing phenotype definitions to maximize coheritability with complex traits in observational data.

WebGWAS provides a web tool to quickly generate approximate GWAS summary statistics for custom phenotypes without requiring individual-level data.

This paper investigates the portability of polygenic scores across genetic and demographic differences, identifying three key gaps in understanding.

The paper introduces a network permutation framework to assess how node degree influences edge prediction in biomedical networks and provides an edge prior as a baseline for performance evaluation.

This paper introduces Indirect GWAS, a method that accelerates biobank-scale GWAS by computing statistics for custom phenotypes using summary statistics, reducing runtime significantly.

The paper introduces hetnet connectivity search to find meaningful relationships between biomedical entities in heterogeneous networks without requiring supervised training data.

This study uses rapid diagnostics and metatranscriptomics to analyze SARS-CoV-2 infection, revealing viral evolution, host responses, and drug interactions in NYC samples.

The paper evaluates multiple compression methods and dimensionalities on gene expression data, finding no single best approach but showing benefits from ensemble methods across different dimensions.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.