Pathum Kossinna

This study maps somatic mutations in B-cell subsets of SLE patients, finding high mutational burden in double-negative B cells linked to disease duration and therapy.

The paper identifies four epithelial transcription states in pancreatic cancer and links their origins to early developmental processes.

The paper introduces scSCOPE, a method for identifying reproducible and functionally relevant marker genes from scRNA-seq data, showing improved consistency across datasets.

iSHARC is a pipeline for analyzing scMultiome data, enabling quality control, integration, clustering, annotation, and regulatory analysis in cancer studies.

This paper introduces edLMM, a method that detects low-effect genetic variants using moderate sample sizes, improving missing heritability estimation.

The paper introduces cLD, a statistic for measuring rare-variant linkage disequilibrium between genomic regions, and shows it identifies novel genomic interactions and associations.

XA4C identifies Critical genes using explainable autoencoders, revealing novel biological insights in cancer gene expression data.

IBAS identifies novel genes associated with complex traits by integrating gene-gene interactions in association studies, showing improved robustness to noise compared to traditional methods.

SCOPE identifies stable core genes and pathways across cancers, revealing shared genome instability pathways and a kidney cancer driver.

The paper introduces XAE4Exp, an explainable autoencoder for gene expression data, identifying non-differentially expressed genes and cancer-related pathways using TCGA data.

This paper shows that combining feature selection and aggregation in GReX limits TWAS power and adaptability, especially with low expression heritability.

kTWAS combines TWAS feature selection with SKAT kernel methods to improve statistical power and identify novel disease-associated genes.