Bioinformatics, computational biology and biomedical informatics are interdisciplinary fields of science that combine life sciences (including biology, medicine and biochemistry) with computer science, mathematics, statistics and engineering (including their subfields such as control theory, information theory, thermodynamics, machine learning and artificial intelligence) to analyse and interpret biological and medical data.

The availability of biological and medical data on an unprecedented scale, and the development of new approaches to derive knowledge from this data has completely changed the landscape of life sciences. In short, modern biology and medical sciences are increasingly becoming data driven sciences, with applications in:

Medicine: AI and machine learning applied to medical and omics data are opening new avenues for diagnostics and prognostics applications. Digital health apps are changing the way that we deliver healthcare and enable patient monitoring and assessment.

Drug discovery: identifying new candidate active compounds, whether small molecule or biologics, is an extremely time (and resources) hungry process. Data driven approaches to identify and prioritise candidates to test, identify new (or refined) indications for existing drugs are already helping to solve this problem.  

Biotechnology: although we have been exploiting living systems to make products for a very long time, advances in our ability to collect and analyse data about these processes and biological components are enabling us to perform bioprocess automation and optimisation at every stage from discovery to full production scale.

Multi-omics in food and crop science: the availability of omics data (from genomics to microbiomics) has changed the way that we look at everything from designing crops with desired traits to evaluating the effects of foods on our bodies.

Assays: there is scarcely a biological assay that hasn’t been made high-throughput (enabling the rapid collection of data from a large number of samples) and/or high-content (enabling the rapid collection of data about a large number of features from each sample), from high-throughput sequencing to cell-based screening assays.

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Insights from the EPO

We asked Jӧrg Wimmer, Patent Examiner at the European Patent Office for his insights around assessing bioinformatics and healthcare informatics inventions at the EPO. Life Sciences Partner Fran Salisbury spoke to Jӧrg and asked:

What constitutes a technical purpose?

How do you identify the closest prior art?
Must my claims be restricted to specific mathematical methods?
Are methods of healthcare administration always excluded from patentability?

Mewburn Ellis All Q&A (1)


Find out more information on the patentability of computer-implemented inventions at the EPO.

Talk to our bioinformatics specialists

Camille Terfve

Associate, Patent Attorney

Chris Casley

Partner, Patent Attorney

Emma Graham

Partner, Patent Attorney

patenting bioinformatics inventions

Patenting Bioinformatics Inventions at the EPO

What is technical and why does it matter?

The EPO uses a very specific approach to the assessment of patentability of inventions often referred to as the “2 hurdles approach”. We explore the second (and often most problematic) hurdle: inventive step (Article 56 EPC). 

Read our guide and find out how mathematical methods and features of presentation of information can be included in the assessment of inventive step.