Tielman T. Van Vleck

This study evaluates the prognostic value of polygenic risk scores for schizophrenia and finds they do not improve outcome prediction beyond clinical features.

The paper identifies gene- and protein-level features distinguishing gain-of-function and loss-of-function mutations using NLP and machine learning on a new database of pathogenic mutations.

The paper identifies 7 novel IBD-causing genes in Ashkenazi Jewish IBD patients using exome sequencing and analyzes their overlap with early-onset IBD and other diseases.

This study shows that natural language processing applied to electronic health records can accurately identify patients with non-alcoholic fatty liver disease and detect those at risk for disease progression.

This study uses unsupervised machine learning on EHR data to identify two subphenotypes of sepsis-associated AKI with differing mortality rates, emphasizing the importance of intra-patient variability.

The paper identifies a common collagen disease in Puerto Ricans using identity-by-descent mapping and genomic data from a health system.