Nowadays, data is often called the new fuel. But it cannot be any kind of fuel. Artificial Intelligence needs not only vast but also deep and well-structured data to find valuable insights. This is precisely what makes the UK Biobank one of the most important resources in biomedical research – and the reason we chose it for AI applications at Ardigen.
In an era of data abundance, the ability to extract actionable insights from complex, multi-layered datasets is becoming a competitive advantage. We’ve worked with population-scale datasets from the UK Biobank to understand how to make the most out of publicly available data. These lessons have shaped how we design data pipelines, train AI models, and collaborate with experts.
On November 20, we scheduled an open, free virtual meeting with Dawid Rymarczyk, PhD – Director of AI Solutions at Ardigen and Bart Smets, PhD – Director Neuroscience Data Science & Digital Health at Johnson & Johnson. We will share more insights on how we leveraged UK Biobank data in a joint project.
You will learn how to:
- Find hidden patient phenotypes and subgroups by applying machine learning to large-scale patient data.
- Use biobank data to look for new indications for registered drugs.
- Identify the target faster thanks to vast proteomic data from Biobank.
Secure your seat at the “From patient data to novel targets: UK Biobank use case” webinar.
Why the UK Biobank Matters for AI in Life Sciences
With over 500,000 deeply phenotyped participants, the UK Biobank has become a gold standard for population-scale datasets. It offers a unique combination of longitudinal clinical data, high-throughput genomics and proteomics, deep phenotyping, imaging data, lifestyle information, and health outcomes. All harmonized and openly accessible to qualified researchers.
UK Biobank explicitly aims to enable “detailed investigations of genetic and environmental determinants of disease” through such open data. Ardigen has embraced this data democratization trend.
This richness opens the door to a new frontier: translating extensive, structured data into biological intelligence. For us, the UK Biobank has become both a benchmark and a blueprint for building AI systems that can process data at scale while retaining scientific relevance.
As our teams often say, it’s not about having more data, but it’s about having better data. Well-annotated, interoperable, and biologically meaningful data is the proper foundation for AI that predicts and explains as well.
Lesson 1: Deep Data Beats Big Data
One of the first things we learned while working with UK Biobank is that volume alone doesn’t deliver insight. What matters is data depth and structure. Numerous samples with sparse or inconsistent features will not enable robust modeling. In contrast, UK Biobank’s dense phenotype and omics layers provide fertile ground for hypothesis generation.
At Ardigen, we’ve developed biomedical knowledge graphs that integrate all UK Biobank summary statistics with dozens of public and proprietary databases – from gene-trait associations to disease ontologies, drug targets, metabolic pathways, and clinical trial results.
By combining large language models (LLMs) with this structured knowledge base, researchers can ask natural-language questions (like “What genes are most associated with Parkinson’s disease?”) and receive structured, interpretable answers. The result is a dramatic reduction in friction between raw data and decision-making.
In one project, this system enabled our team to identify novel drug targets that previous manual reviews had missed. A redesigned data platform also led to 20× faster model training and up to 50× shorter turnaround time for hypothesis testing.
Speedup: Up to 50× faster data analysis workflows.
Accuracy: ~4× improvement in prediction precision.
Usability: Domain experts without programming skills can access complex results.
Lesson 2: Integration Unravels Biology
A single layer of factors rarely drives disease. That’s why integrating multi-omics with clinical and lifestyle data is essential. At Ardigen, we approach complex diseases like neurodegeneration or autoimmune disorders as systems-level phenomena. This requires modeling interactions between genotype, protein abundance, metabolic signatures, and clinical progression.
Using UK Biobank’s proteomic and phenotypic datasets (e.g., neurodegenerative cohorts of 50,000+ patients), we trained multitask AI models that could classify over 400 diseases in parallel. But the raw data alone was not enough. We curated data using our domain knowledge, corrected batches, and selected features to ensure model quality.
Our deep learning models predicted disease states more accurately, but they also revealed patient subgroups with distinct molecular profiles. These were not apparent in traditional clustering. Thanks to explainable AI methods, we could pinpoint which proteins differentiated these clusters. This means an easier way to find new biomarker candidates and potential stratification criteria.
Result: >90% of classification tasks improved over baseline models.
Insight: Patient subtypes emerged that correlated with differential risk and progression.
This case confirmed what we’ve long suspected: when you combine biological relevance with AI scalability, the pave to discovery occurs much faster.
From Biobank Data to New Patient Treatment: What’s Next?
The UK Biobank has shown us what’s possible when large-scale biological data is structured, accessible, and deeply annotated. We can safely say that it serves as a template for the next generation of biobanks and population health initiatives. Although you must know, it’s not a free data source.
At Ardigen, we apply our experience with UK Biobank’s data across new AI platforms. Whether it’s building generative models of antibody sequences or decoding cellular responses to perturbations, the principles remain the same:
- Structure your data.
- Validate your features.
- Let AI scale your data cleaning.
- Keep humans in the loop.
The growth of data is already enormous at this stage and will certainly become an increasingly greater technological challenge. If you are looking for someone to help you keep up in this race, you have come to the right place. Let’s discuss whether Biobank data can be used in your project.
Turn medical data into fuel for drug development. Learn how to get the most from the UK Biobank database with AI.
Join our live event with a Q&A session on Nov 20, 2025, 11:00 am ET.
Author: Martyna Piotrowska
Technical editing: Ardigen expert: Dawid Rymarczyk, PhD
Bibliography:
- Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779
- Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today. 2021;26(4):1040–1052. https://doi.org/10.1016/j.drudis.2020.11.037
- UK Biobank. Data releases [Internet]. 2025 [Cited 2025 Oct 17]. [Available from:] https://www.ukbiobank.ac.uk/about-our-data/data-releases/
