Judith Bernett

Inferring tested-but-undetected pairs from prey lists of 5,530 published interaction screens yields 97 million tested human protein pairs and 18,982,508 non-interactions (≥3 tests) for calibrating screens and predictors.

This paper refines the Tangram spatial mapping tool to improve consistency and reliability in mapping single-cell RNA-seq data to spatial transcriptomics data.

This paper evaluates drug response prediction models and finds that deep learning models perform similarly to simple baselines, highlighting issues in current model evaluation practices.

This study shows that sequence-based PPI prediction models using ESM-2 embeddings plateau at 0.65 accuracy, suggesting structural information may be needed for better performance.

The paper evaluates deep learning models for protein-protein interaction prediction, finding they rely on data leakage and sequence similarity, and advocates for baseline comparisons.

The paper compares differential expression analysis methods for CyTOF data, finding that median-based methods are reliable for non-zero-inflated data while methods using full distributions handle zero-inflation better but may overpredict.