David Wissel

The paper evaluates five single-cell RNA sequencing pipelines using Omnibenchmark, highlighting differences in parameter implementation across tools.

This study benchmarks long-read RNA-seq across bulk and single-cell platforms, technologies, and sequencing depths, identifying optimal methods for transcript discovery and quantification.

This study benchmarks PacBio Kinnex long-read RNA sequencing against Illumina short-read RNA-seq, finding Kinnex provides more accurate transcript discovery and quantification with comparable gene-level performance.

The paper develops a workflow using long-read RNA-seq and MS data to construct haplotype-resolved proteomes, enabling detection of variant and allele-specific protein isoforms.

CancerFoundation is a single-cell RNA-seq foundation model that outperforms existing models in batch integration and drug response prediction, even when trained on a smaller dataset.

SurvBoard benchmarks multi-omics cancer survival models, showing statistical models outperform deep learning in calibration and that pan-cancer training improves performance.

The paper sparsifies high-dimensional multi-omics survival models using knowledge distillation with 1-penalized linear models, achieving performance comparable to complex teachers while using fewer features.

This paper introduces a hierarchical autoencoder for multi-omics cancer survival analysis, showing improved performance through soft modality selection.