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.