Osman Uğur Sezerman

The paper reports a pathogenic inversion in UNC13D and analyzes chromosomal inversions across multiple datasets to identify their molecular characteristics and implications in genetic diseases.

The paper investigates the relationship between PDAC stromal subtypes and neural invasion using single-sample classifiers and RNA-seq data, finding activated stroma is enriched in neuro-invasive samples.

The paper functionally annotates genomes of ISS-isolated bacteria and identifies adaptations to space conditions through comparative genomics and proteome analysis.

This study applies collaborative network analysis to Huntington's disease transcriptomics data to identify enriched pathways and generate hypotheses about disease mechanisms.

predatoR is an R package that predicts mutation impact using network-based machine learning, achieving high accuracy on benchmark datasets.

The paper identifies secretion type-associated motifs from pathogenic bacterial sequences to improve vaccine candidate identification.

driveR prioritizes cancer driver genes using somatic genomic data through a multi-task learning model, outperforming existing methods across 28 datasets.

The paper harmonizes clinical interpretations of cancer genomic variants across six knowledgebases, improving overlap and matching with patient cohorts.

pathfindR identifies active subnetworks from gene lists and performs pathway enrichment analysis, clustering, and visualization for biological interpretation.

The paper introduces DAPath, a tool for analyzing GWAS SNPs in pathway contexts to study Behçet’s Disease aetiology in Japanese population, identifying key pathways impacted by modest SNPs.