Shawn N. Murphy

The paper analyzes pathogenic variant frequencies in the All of Us cohort by ancestry, revealing disparities and potential biases in genomic data.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This study develops a high-throughput phenotyping method using temporal sequences from EHR data, achieving superior classification performance compared to conventional methods.

sureLDA automates phenotype labeling in EHR data using topic modeling, achieving high accuracy and robustness across various phenotypes.

This paper develops a clustering-based method to detect implausible values in EHR data, showing it has high specificity and fewer false positives than conventional approaches.

This paper develops an automated high-throughput phenotyping method (MAP) that improves phenotype accuracy and scalability for PheWAS studies.

This paper evaluates a semi-supervised encoding approach for outlier detection in clinical observation data from EHRs, finding that semi-supervised encoders outperform conventional autoencoders.

This paper validates EHR-based BD phenotyping algorithms by showing their genetic correlation with traditional BD cases, suggesting they identify genetically similar cases.