Computational Biology & AI
Our computational biology team handles the dry-lab side of every project, from bioinformatics pipelines and statistical analysis to AI-driven models designed to uncover the signals that matter in your data. Every result is interpreted by our scientists and connected back to the biology—never delivered as a black box.
Alignment, quantification and QC, version-controlled end to end.
Combining genomics, transcriptomics and other layers into one coherent model.
Inference models to prioritize candidate biomarkers from complex datasets.
How this computational work applies in each sector.
Skin biology, transcriptomics and microbiome profiling for dermo-cosmetic innovation.
Multi-omics profiling of the tumor microenvironment for actionable biomarkers.
Molecular characterization to support cell engineering and gene therapy programs.
Host-pathogen interactions and microbial ecology for infectious disease research.
Integrated multi-omics for patient stratification and biomarker validation.
Translational biomarker pipelines to accelerate discovery programs.
Have a dataset that needs interpreting?
Working from your data, what you receive, and how a signal is held to be real.
Yes, and it is a large part of what the computational team does. We start from your raw or processed files, re-run the quality control so that the starting point is documented, and tell you what the data can and cannot support before proposing an analysis.
A report that answers the biological question, the processed data and result tables, and the figures. The pipeline versions and reference genome are stated so the analysis can be repeated. Anything beyond that — code, notebooks, an interactive dashboard — is agreed in the study documentation rather than assumed.
By deciding the analysis plan before looking at the outcome, and by separating discovery from validation. A signal found on one set is tested on data that did not contribute to finding it. Where the cohort is too small for that, we say so and report the finding as exploratory — which is a result, not a failure.
They are fixed per study and written down, because two analyses run on different annotation versions are not comparable and the difference does not show in the report. We state the reference build, the annotation release and the pipeline version with the results.