**Why**:
Biological data is becoming more complex, and this complexity impedes our ability to actually find new hypotheses and get to deeper insight. Many researchers struggle to fully explore and understand their multimodal datasets (genomics + transcriptomics + phenotyping + experimental conditions + climate + ...). We need to help biologists embrace the complexity and uncertainty of their data instead of pruning it down until only simplified patterns remain.

**How**:
Expertise in:
- interactive data visualisation tools and visual analytics
- topological data analysis
- bioinformatics
- multi-omics data integration

In general, we work closely with the domain expert (i.e. biologist) in short iterations so that the expert can continuously adjust their hypotheses.

**What**:
We have contributed to many cross-disciplinary collaborations with biological experts, and have won several biological data visualisation competitions. These include projects on functional brain networks, post-translational protein modifications, extracting social interactions in pigs based on feeding behaviour, etc.
We are currently setting up a European Marie Curie Doctoral Network on the topic of biological data visualisation.

Agriculture
Animal Biotechnology
Bioinformatics
Biology of interactions
Conservation Biology
Crop Production
Crop Science
Dairy Science
Diagnosis (microarray and biosensing devices for early / precise detection of diseases)
Evolutionary Biology
Genetics and heredity (excluding Medical Genetics)
Genomics
Livestock Production
Microbiology
Molecular Biology
Other Biological Sciences
Phylogenetics
Plant Biotechnology
Plant Protection
Plant Selection
Selection and breed improvement
Selection using molecular markers
Technology of genetic engineering (crops and livestock)
Virology

We are keen to contribute to projects that focus on complex, multi-modal data integration, particularly in fields related to agriculture, biodiversity, and the effects of climate change. The ideal project would involve one of the following:
- Multi-modal data sources: integrating diverse data types (e.g. genomic data, environmental sensor data, weather patterns, ...) to explore cross-domain insights.
- Uncertainty: addressing challenges in uncertain or noisy data, such as missing values or incomplete data streams, and developing methods to incorporate uncertainty into interpretation and uncovering hidden assumptions.
- Agricultural sustainability and biodiversity: investigating the impact of climate change on crop yields, soil health, and biodiversity
- Complexity in data: engaging with datasets where high-dimensionality, heterogeneity, and complex interdependencies require new methods for analysis and visualisation.

Any project that embraces the complexity and uncertainty of biodata to uncover deep insights, rather than simplifying it, aligns with our core interests.

omics, data integration, visualisation, bioinformatics

-