Yan W. Asmann

This study identifies genetic risk factors for FTLD-TDP subtypes using whole-genome sequencing and meta-analysis across multiple cohorts.

Immunopipe provides a flexible pipeline for integrated analysis of scRNA-seq and scTCR-seq data to enhance understanding of T-cell immunity and immunotherapy.

This study uses single-cell multi-omics data to identify disrupted glial gene regulatory programs in Alzheimer's disease using interpretable machine learning.

This study evaluates and optimizes the interpretability of machine learning models applied to transcriptomic data by assessing explainers on MLP and CNN architectures and identifying tissue-specific genes.

The paper identifies SERPINA5 as a novel tau binding partner in Alzheimer's disease by analyzing hippocampal gene expression and neurofibrillary tangles.

The paper identifies four genes, including two novel ones, co-expressed with TREM2 in Alzheimer's disease using whole-exome data from ADSP.

The paper identifies cell-intrinsic differentially expressed genes in Alzheimer's disease by analyzing bulk-RNAseq data and comparing results to single nucleus RNAseq.

This study introduces a computational method to classify cancer driver genes as oncogenes or tumor suppressors based on mutation patterns across 33 cancer types, identifying novel genes and revealing context-specific functions.

The paper describes a Swift/T wrapper for the GATK variant calling workflow to improve portability and maintainability across cluster environments.

The paper identifies dark and camouflaged gene regions in the human genome, shows how long-read sequencing reduces their prevalence, and applies an algorithm to uncover disease-relevant variants in Alzheimer's Disease Sequencing Project data.