Yoon Sim Yap

Systematic benchmarking of multi-modal approaches for tumor-naive ctDNA detection and quantification

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.

Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA

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

Fragle: Universal ctDNA quantification using deep learning of fragmentomic profiles

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

Marker-free characterization of single live circulating tumor cell full-length transcriptomes

The paper introduces unCTC, an R package for unbiased identification and characterization of CTCs from scRNA-Seq data, validated on breast CTCs.

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

This paper develops a targeted cfDNA sequencing approach for detecting genomic alterations in breast cancer using UMIs and bioinformatics pipelines, showing high concordance with tumor sequencing.

Integrative analysis and machine learning based characterization of single circulating tumor cells

The paper analyzes single-cell expression profiles of circulating tumor cells (CTCs) across cancers, identifies EMT-related patterns, and develops a classifier to recognize CTCs using PBMC transcriptomes.