Spotlight on

Bioinformatics & Digital Health

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.

Patent Landscape in Bioinformatics and Digital Health: a data-driven analysis

In our Special Report we set out to collect data on the patent landscape in the fields of bioinformatics and digital health, to see whether the growth we and our clients see in the field is reflected in the data and whether insights can be gained that will assist in designing better, more informed IP strategies. Access our report to find out more about the dramatic increase in the number of patents and applications published in relation to bioinformatics and digital health, particularly in the US and China.

Patent Landscape in Bioinformatics and Digital Health Special Report 2021-2
Download the report

Read our Bioinformatics Blogs

The brilliant dawn of AI drug discovery

The brilliant dawn of AI drug discovery

Computer simulations are solving challenges previously thought uncrackable.

The link between the microbiome and chronic liver disorders

The link between the microbiome and chronic liver disorders

Guts UK is the charity for the digestive system. It funds scientific research such as Nicholas Ilott’s investigation into the link between the microbiome and chronic liver disorders, as Mewburn Ellis ...

Gene sequencing accelerates with custom hardware

Gene sequencing accelerates with custom hardware

The cost to sequence a genome continues to drop exponentially. Over the past 20 years, the cost to sequence a human genome has dropped from the estimated $3 billion of the Human Genome Project, to a ...

Motion capture and AI in healthcare

Motion capture and AI in healthcare

When most of us hear the term motion capture, we tend to think of special effects in films and actors rolling around in suits covered in ping-pong balls. However, two recent publications in Nature ...

AI and Tech innovation stands out in a slow year for pharma deals

AI and Tech innovation stands out in a slow year for pharma deals

Last year saw a drop in the number and value of deals performed in the biotech and pharma sector compared to previous years. Despite this, deals involving companies focussed around bioinformatics and ...

Meet the team: Emma Graham, Partner and Patent Attorney in the Engineering team, London

Meet the team: Emma Graham, Partner and Patent Attorney in the Engineering team, London

As part of our 'meet the team' series, we talk to Emma Graham about her love of patent drafting, the challenges of patenting software and the fastest growing areas of development in healthcare ...

Learn More

The Technologies Behind Bioinformatics

Bioinformatics and medical informatics encompass many different fields of application and underline technologies that are equally diverse. However, many of these will have aspects in common, whether in terms of the data that they are using (e.g. data from genomics, proteomics, transcriptomics, metabolomics, imaging (medical, or from lab assays), biological or physiological sensors, etc.), or in terms of the approach to exploiting the data (e.g. machine learning, AI, mathematical simulations, statistical modelling, etc.).

AI and machine learning powered medicine

Healthcare is changing beyond recognition, in part driven by the fact most of us have powerful computers in our pockets, on our wrists, and in our homes. Digital health apps are being developed to leverage this capacity, to make patient monitoring, assessment, etc. cheaper, more efficient and less time consuming.

Modern AI is very good at picking out patterns in images, some of which even escape the human eye. One of the many areas where this has shown promise is in image analysis (PET, radio, MRI, ) for diagnostics and prognostics purposes, e.g. to identify and monitor the growth of tumours from scans, to identify signs of diseases (e.g. nodules caused by diseases such as pneumonia in chest x-rays), etc.

The availability of large amounts of omics data (typically along the central dogma, i.e. genomics, transcriptomics, proteomics and combinations thereof) has opened new possibilities for diagnostics and prognostics, enabling patient stratification (the identification of subgroups of patients, such as subgroups that are likely or unlikely to benefit from specific therapies or subgroups with different prognosis, etc.), increasing diagnostic accuracy, reducing the need for invasive tests for e.g. cancer and pre-natal tests, etc. This underlines much of the precision medicine revolution.

Computational approaches to drug discovery

Successfully getting a new drug to market is becoming increasingly expensive, and increasingly infrequent. Data driven approaches including AI, machine learning, modelling, in silico optimisation etc. are offering new hope in tackling this problem.  

One promising area of application of bioinformatics approaches is in drug repurposing (identifying new therapeutic applications for existing drugs) and drug efficacy screening (predicting targets and efficacy for candidate drugs). AI, machine learning and network medicine approaches, for example using comorbidity maps, pharmacogenomics, combined drug targets and interactome networks analysis etc. have the potential to make the drug discovery process cheaper, safer and faster.

Another potentially promising area is in AI powered drugs and a biologics design. Technologies such as in silico evolution of proteins with a desired function, in silico identification of drug candidates with desired targets or properties, simulation of target protein molecular dynamics to understand drug-able features of the target, etc. can help us guide the traditional drug candidate screening process. These process can essentially act like the light strip in a dark airplane aisle, guiding us towards where we need to go so we no longer have to bump into every single obstacle on a random walk that may or may not lead to the destination. 

Bioprocess automation and optimisation

The control, monitoring and optimisation of processes involving biological material (such as e.g. production of biologics from cells in culture) used to be highly experience and trial-and-error based. Computational tools using design of experiments approaches, multivariate analysis, automated monitoring and interpretation of process and biological parameters for in-line, etc. are making this process more reproducible and more efficient, ultimately leading to the reliable production of high quality biological products.

Laboratory automation is becoming an increasingly important part of life in labs whether in research, development, or quality control, in an effort to increase reproducibility and efficiency of bioprocesses. This involves software and hardware improvements that come out of multidisciplinary teams involving engineers, life scientists, computer scientists, physicists, etc.

Multi-omics in food, crop science, environmental science, personal care and hygiene

Our understanding of how food impacts our health has greatly benefited from the availability of omics data, from genomics to metabolomics and microbiomics. The relatively young sciences of foodomics and nutrigenomics have helped us understand the relationship between dietary patterns and genetic factors in obesity, how food bioactives can have an effect on cancer, and how genetic engineering of crops can help us tackle malnutrition.

In agriculture, omics data is being used to design crops that have traits of interest such as drought or pest resistance, increased yield or modified composition for the production of biofuels or biopolymers, etc.

In personal care and hygiene, microbiomics approaches are helping us understand the human and animal microbiota, their impact on health, and how they can be influenced to tackle acne, skin aging, etc.

In environmental sciences, omics data is being used to help us design solutions for water purification, recycling, and bioremediation (the use of microorganisms to clean a contaminated site).

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?


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

Forward-looking for Bioinformatics

Bioinformatics now has a place in virtually every life science application, and this transformation of life sciences from a small scale mostly lab-based discipline to a data driven, large scale systematic quantitative discipline is not about to stop. Significant advances in many fields have already been seen, and there is so much exciting work going on at the moment that it would be impossible to choose just one “area to watch”. However, emerging trends in the field point towards greater emphasis in the future on: 

Remote healthcare solutions

There is no doubt that the pandemic has accelerated the trend towards remote healthcare solutions and this looks set to continue as data-driven technology plays an increasingly significant role in all our lives. Our Special Report, Patent Landscape in Bioinformatics and Digital Health, shows that this revolution was already well underway before the pandemic hit. It will be interesting to see how these trends have been influenced by the pandemic and how they will develop as we move into the post-pandemic era.

Data integration

Combinations of information from multiple omics sources, image analysis, digital health applications, medical records etc. are likely to quite literally help us connect the dots to answer many pressing questions. For example, the young field of radiogenomics looks at how medical imaging and genomics data can be combined to provide better diagnostic and prognostic tools. 

Cell free DNA

The potential for cell free DNA (circulating DNA that is released by cells and can be detected in liquid biopsies) to be used as a tool to monitor or diagnose cancer or detect chromosomal abnormalities in utero has already resulted in concrete improvements to patient care, but we have only found the tip of the iceberg of potential.

Single cell technologies

Single cell sequencing technologies have been available for a few years now, shedding some light on cellular heterogeneity and its implication in everything from cancer evolution to development. The single cell revolution is not going to stop there, with multimodal approaches being developed (e.g. combining single cell proteomics and single cell transcriptomics), new single cell screening platforms enabling screening on a brand new scale, etc.


Applications of AI to various life sciences problems, and especially to image analysis, are coming up almost daily. In short, we are not done seeing the word “AI” in the news, and soon we will see it in routine healthcare.

Find out more about our expertise in the fields of bioinformatics and digital health.

Talk to our Bioinformatics Specialists

Patenting Bioinformatics Guide

Patenting Bioinformatics Inventions at the EPO

Download the guide

Many inventions in the field of bioinformatics are computer implemented methods or systems configured to implement such methods. The EPO uses a very specific approach to the assessment of patentability of such inventions, which is sometimes referred to as the “2 hurdles approach”.

Features of a mathematical method and features of presentation of information will be completely ignored in the assessment of inventive step, unless it can be shown they serve a technical purpose in the context of the invention. Here we look at how to do this for mathematical methods and for the presentation of information.