Verena Chung

The paper presents a benchmarking challenge to predict Alzheimer's disease neuropathology from single-nucleus transcriptomics using human and AI models, finding that top methods achieve near-perfect categorical staging predictions.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

BioLitMine helps researchers find relevant biomedical literature by leveraging MeSH terms and gene annotations to improve search accuracy and identify co-cited genes and authors.

SNP-CRISPR is a web tool that designs CRISPR sgRNAs for SNP-containing alleles, accounting for genetic variations in non-reference genomes and across genetic backgrounds.

The paper introduces iProteinDB, a database integrating Drosophila phosphoproteomics data with other PTM datasets to identify conserved phosphosites across species.