Shilpa N. Kobren

This paper develops a specific pipeline to detect balanced reciprocal translocations in genomic data and applies it across multiple cohorts to assess their prevalence and diagnostic impact.

This paper introduces SimPheny, a framework that uses patient-to-patient phenotype matching to prioritize genes for rare disease diagnosis, outperforming existing methods in cases with limited phenotype associations.

This paper demonstrates that ensilication preserves DNA integrity at ambient temperature, enabling diagnostic-quality long-read sequencing without cold-chain infrastructure.

This study identifies rare germline variants in immune and drug target genes that are enriched in cancer exceptional responders compared to typical patients, suggesting potential biomarkers for improved treatment outcomes.

This paper optimizes variant prioritization parameters for Exomiser and Genomiser to improve diagnostic yield in rare disease cases using WES and WGS data.

The paper presents a joint genomic analysis of UDN patients using whole genome sequencing and new statistical methods to prioritize disease genes, revealing new disease associations and releasing a software tool for cohort analysis.

SHEPHERD uses few-shot learning and a knowledge graph to improve diagnosis of rare genetic diseases using simulated data and real-world patient cohorts.

VarPPUD improves the prioritization of pathogenic variants in undiagnosed genetic disorders by distinguishing likely from unlikely disease-causing variants using machine learning.

RExPRT predicts pathogenicity of tandem repeat expansions with high precision, identifying ~30,000 likely pathogenic TRs in the reference genome.

This paper simulates patients with novel genetic conditions to evaluate gene prioritization methods in diagnosing rare disorders.

The paper introduces PertInInt, a method that identifies cancer genes by analyzing somatic mutations in interaction sites across 10,037 tumor samples, revealing disrupted interactions as a common cancer mechanism.

The paper introduces a method to identify ligand-binding positions within protein domains using co-complex structures, resulting in a resource called InteracDome that aids in understanding disease-related mutations.