Brandon Jew

Mapping the evolution of computationally designed protein binders

This paper evolves computationally designed protein binders using in vivo hypermutation and maps their sequence-affinity landscapes to understand affinity improvements and mutation interactions.

Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

The paper benchmarks two methods for detecting cell-type-specific differential DNA methylation, finding TCA superior to CellDMC and discussing best practices for cell-type resolution analysis.

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

The paper introduces DIEM, a semi-supervised machine learning classifier to remove debris-contaminated droplets in snRNA-seq, improving clustering quality across three datasets.

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Bisque estimates cell composition in bulk RNA-seq using single-cell data, improving accuracy and efficiency over existing methods.

ForestQC: quality control on genetic variants from next-generation sequencing data using random forest

ForestQC improves variant quality control by combining filtering and machine learning, outperforming existing methods on whole-genome sequencing datasets.

Preoperative predictions of in-hospital mortality using electronic medical record data

This paper develops a machine learning model using EMR data to predict in-hospital mortality preoperatively, achieving higher accuracy than existing clinical scores.

Leveraging allele-specific expression to refine fine-mapping for eQTL studies

This paper develops a statistical method that uses allele-specific expression and eQTL data to improve fine-mapping of causal variants for gene expression, achieving higher specificity than eQTL-only approaches.