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DylanLawless/README.md

Dylan Lawless, PhD

Biomedical evidence, software, infrastructure, and product delivery.

I work across genomics, multi-omics, clinical data, statistical inference, secure computing, and scientific software. My focus is the chain from evidence to usable systems: data structure, method, provenance, documentation, interface, governance, and decision support.

Professional profile: lawless.ch
Major commercial outputs: Switzerland Omics
Main academic outputs: ORCID · Google Scholar
Design and product systems: design.lawless.ch


Current focus

I build verifiable analytical systems for precision medicine, diagnostics, biomarker research, and biomedical data infrastructure. I am especially interested in converting our basic research into sucessful products for biotech, pharma, and diagnostics. That work depends collaboration, infrastructure, documentation, IP awareness, regulatory judgement, and a clear understanding of how scientific products reach real users.

Core areas:

  • Statistical genomics, Bayesian inference, and uncertainty quantification
  • Rare disease, infectious disease, host genetics, and precision medicine
  • Whole-genome sequencing, RNA-seq, proteomics, metabolomics, and EHR-linked data
  • Reproducible R, Python, SQL, Linux, HPC, and cloud workflows
  • Scientific software, structured reports, evidence databases, and product-facing systems
  • Secure and governance-aware biomedical data infrastructure

Academic foundation

My software and product work is grounded in academic and clinical research across genomics, immunology, infectious disease, rare disease, and translational medicine.

Period Institution Focus
2023 to present Universitäts-Kinderspital Zürich and University of Zurich Intensive care and neonatolgy, Translational medicine, paediatric critical care, clinical genomics, multi-omics, and secure biomedical data infrastructure
2018 to 2023 EPFL Global Health Institute Fellay lab, Human genomics of infection and immunity, host-pathogen biology, statistical genetics, and translational cohort analysis
2015 to 2019 University of Leeds School of Medicine and St James’s University Hospital PhD in Medicine, rare immune disease discovery, genomic interpretation, and functional validation
2014 to 2015 EPFL Global Health Institute Ablasser lab, Innate Immunity
2013 to 2014 Trinity College Dublin MSc Immunology, first class honours
2009 to 2013 University College Cork BSc Microbiology, host-pathogen biology, and immunology

Selected awards include the FKZ Children’s Research Centre research prize, Microsoft Azure Research Award, Wellcome Genome Campus visitor research grant, and University of Leeds postgraduate research scholarships.


Selected software and outputs

Output Scope Links
QuantBayes Studio Evidence-based conclusions for AI, science, engineering, and regulated work site
QuantBayes Bayesian quantification of genomic evidence sufficiency with posterior intervals site · article · CRAN · Zenodo
CRAN version CRAN downloads
Archipelago Variant set association statistics and visualisation for complex genomic studies site · article · CRAN
CRAN version CRAN downloads
VCFheader VCF header parsing and structured standalone HTML reporting site · browser · CRAN
CRAN version CRAN downloads
Evidence ratio Likelihood-based evidence scale for clinical trials, studies, and analytical results site · CRAN
CRAN version CRAN downloads
PanelAppRex AI Harmonised disease-gene panels from structured clinical and genetic queries site · article · repository · dataset
GitHub stars
Qualifying variant database Reusable YAML criteria for reproducible genomic variant interpretation site · article
Genomic Vault Long-term custody and controlled access for genomics and precision medicine platform
IEI genetics database Genetic panels and prior probabilities for disease-causing variants in inborn errors of immunity site · article

Repository map

This account contains research code, R packages, analysis workflows, documentation sites, data products, and product prototypes.

Main repository areas:

  • Statistical modelling, machine learning, and reproducible analysis
  • Genomics, multi-omics, and biomedical evidence systems
  • R and Python packages for scientific computing and reporting
  • Secure data workflows, clinical research infrastructure, and structured outputs
  • Web products, documentation systems, and scientific interfaces

Technology stack

Languages          R · Python · SQL · Bash · C · Rust · TypeScript
Data               WGS · RNA-seq · proteomics · metabolomics · EHR-linked data
Statistics         Regression · Bayesian inference · machine learning · statistical learning · uncertainty quantification
Systems            Linux · HPC · Docker · Apptainer · Nextflow · Snakemake
Platforms          PostgreSQL · Supabase · Next.js · React · Vercel · APIs
AI                 retrieval · embeddings · structured extraction · agentic workflows
Governance         provenance · auditability · secure data · GDPR/FADP · IVDR-aware workflows

Pinned Loading

  1. DylanLawless.github.io DylanLawless.github.io Public

    Focused on novel discoveries in human health with specialities in human genetic disease, infectious disease, rare and common disorders, and the interaction of host-pathogen genomics.

    HTML

  2. spss_exome_vsat spss_exome_vsat Public

    This repository contains all the code, non-sensitive data, and resources used in "Rare variants in infection response protein pathway associated with sepsis in children".

    JavaScript

  3. ProteoMCLustR ProteoMCLustR Public

    Proteome clustering with MCL algorithm in R. A tool for clustering and analyzing protein-protein interactions using the MCL algorithm.

    R

  4. ACMGuru ACMGuru Public

    What good are standards if they are not automated?

    HTML 1

  5. archipelago archipelago Public

    Archipelago plot method for illustration of variant set association test statistics

    R 1

  6. R-reinforcement-learning-an-introduction R-reinforcement-learning-an-introduction Public

    R implementation of Reinforcement Learning: An Introduction, 2nd edition by Richard S. Sutton and Andrew G. Barto

    R