Projects
Projects and focused work
This page brings together standalone projects and focused methods from larger experiences. Each card makes its original setting clear.
Project work
Focused project work
The first three cards are independent technical projects. The remaining cards identify the experience that gave each deliverable or method its context.
Liver MRI Methods
A reproducible multiphasic MRI study covering image geometry, registration inspection, dynamic-contrast features and a model failure that now guides the next debugging work.
Bioinformatics workflowReproducible RNA-seq Service Workflow
A Nextflow DSL2 service exercise taking a paired public RNA-seq question from validated intake through DESeq2, checked results and a provenance-rich handover.
Self-directed projectClinical informatics trial operations
A synthetic study workflow connecting protocol requirements, relational data checks, operational metrics and an interactive dashboard.
Final-year project methodExploratory modelling approach
Cohort construction, ICD/HES-derived feature engineering, model comparison and a staged mutual-information continuation that stopped when the data did not support further searching.
Health Innovation East deliverableAI Toolkit
Internal guidance-support material translating AI/MedTech governance, evidence expectations, clinical safety and data requirements into advisory questions.
Health Innovation East methodMarket intelligence and competitor analysis
Structured market, competitor and horizon-scanning work for live innovation questions, shaped around evidence quality and pathway fit.
Internship methodComputational toxicology data preparation
Dose-toxicity data mapping and structured preparation work from the ConsoneAI/DioScor research internship.
Exploratory methods
Future biological evidence methods
Exploratory methods focused on weak-signal recovery. This strand grows out of the final-year project's rare-disease limits: biologically informed, provenance-aware methods for hypothesis generation, study-design support and measurement prioritisation when the data is sparse and the next evidence decision matters.
Concept-stage methods thinking, not a validated platform or clinical tool.
- RecoverUseful signal
- PrioritiseNext measurement
- Recovering useful signal without pretending sparse data is richer than it is.
- Using biological context to prioritise what to measure next.
- Generating sharper hypotheses and follow-up study designs.
- Stress-testing assumptions with negative controls, ablations and disproof-first checks.
- Keeping observed evidence, derived features and augmented assumptions clearly separate.
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