Anders J. Skanderup

This study benchmarks tumor-naive ctDNA detection and quantification methods using WGS and methylation data from cancer patients, identifying performance trade-offs and optimal integration strategies.

This study identifies genomic and transcriptomic features on chromosome 1p associated with metachronous metastasis risk in ccRCC and develops a predictive model.

The paper benchmarks 9 somatic variant calling algorithms on cfDNA samples from cancer patients, identifying optimal methods based on ctDNA levels and sequencing depth.

GCfix corrects GC bias in cell-free DNA data across diverse coverages, outperforming existing methods with two new performance metrics.

This study introduces NDRDiff, a computational framework for analyzing cfDNA fragmentation profiles to infer gene expression and identify differentially expressed genes in cancer.

Fragle quantifies ctDNA from fragment length profiles using deep learning, showing high accuracy and improved detection limits across multiple datasets and sequencing types.

This paper introduces PUREE, a method to estimate tumor purity from gene expression data, achieving high accuracy across multiple cancer types and outperforming existing methods.

MutSpot identifies non-coding mutation hotspots in cancer genomes by analyzing epigenetic and sequence features across thousands of genomes.

SMuRF improves somatic variant calling accuracy by combining multiple callers using Random Forest, working across cancer types and data types.