Dylan Lawless

The paper integrates multi-omics data from pediatric sepsis patients to identify a reproducible inflammatory host-response axis associated with disease severity.

The paper introduces a framework for defining and reusing qualifying variants (QVs) in genomic analysis to improve transparency, reproducibility, and interoperability across pipelines.

The paper introduces a Bayesian model to quantify the sufficiency of genomic variant evidence for Mendelian diseases, enabling consistent interpretation across institutions.

The paper introduces the Archipelago method to visualize variant set association test statistics by assigning genomic coordinates to P values, enabling combined visualization of set-level and individual variant associations.

PanelAppRex aggregates and standardizes disease gene panels to enable efficient search and integration into bioinformatic workflows, recovering causal genes in all benchmarked cases.

The paper identifies genetic pathways and variants associated with sepsis susceptibility in children through genomic analysis of a paediatric cohort.

This paper identifies a genetic locus associated with sepsis susceptibility in children using a genome-wide association study of a national cohort.

This paper introduces a statistical framework to quantify prior probabilities of disease-causing variants in inborn errors of immunity, revealing top genetic contributors and validating results in national cohorts.

This paper develops a reinforcement learning framework to estimate the probability of observing genetic variants in disease using genomic metrics, showing predictive accuracy and effective learning.

The paper investigates how human genetic variation influences Epstein-Barr virus (EBV) sequence diversity by analyzing paired viral and human genomic data from HIV-coinfected individuals.

The paper predicts clinically relevant variants in RAG1 and RAG2 by analyzing mutation rates and residue frequencies to identify disease-causing mutations.