What do solutions for acne and machine learning for rare disease prediction have in common?

Posted on: 22/05/2019

These are just two of the problems KTN is calling for researchers to work on at its Industrial Maths in AI and Health event, 26-28 June in Manchester.

KTN is looking for UK university researchers interested in participating in a study group that will be held from 26-28 June in Manchester in partnership with Manchester University.  It will consider issues using Artificial Intelligence (AI) in Health, applying mathematical sciences to develop solutions to challenges.


The KTN Industrial Maths study groups will provide a platform for researchers to consider industry problems and posit solutions derived from mathematical sciences.  There is a call out for researchers to participate in the study group and the challenges are outlined below.


Secure Machine Learning for Rare Disease Prediction presented by Mendelian

In the UK there are an estimated 3.5 m rare disease patients who, on average, wait five years for a diagnosis and are referred to seven different doctors – a significant burden on our healthcare system.  The problem is that rare diseases are difficult to diagnose.  Mendelian has spent several years creating tools for clinicians, geneticists and healthcare systems who manage rare diseases and is interested in exploring machine learning methods that are effective and secure on siloed data. Read more about the challenge.


AI-enabled Prebiotic Discovery Platform presented by Unilever

Cosmetic conditions are a daily concern for people worldwide.  Conditions such as acne and psoriasis can have a debilitating effect on self-esteem, particularly in adolescents.   Traditionally personal care products treat these conditions with an indiscriminate reduction of microbial load leading to a reduction in symptoms.  This challenge is to explore where Artificial Intelligence and machine learning algorithms can be used to identify abundant and prevalent organisms associated with selected cosmetic conditions; the determination of key metabolic differences between health associated and condition associated organisms; and identification of compounds (cosmetic ingredients/natural products) that take advantage of these metabolic dependencies to promote (prebiotics) the growth of health-associated organisms such that they outcompete and reduce absolute numbers of condition-associated organisms.  Read more about the challenge.


Development of a Formalin-Fixed Paraffin-Embedded (FFPE) artefact filter for RNAseq based biomarkers presented by Almac Diagnostic Services

Formalin-Fixed Paraffin-Embedded (FFPE) tissue is the most common format for archiving solid tumour pathology specimens after surgery, thus providing a potentially invaluable resource for translational clinical research.  However, due to the formalin fixation process causing degradation and chemical modifications, FFPE samples are typically poor quality.  The objective of the challenge is to develop a machine learning approach to correct from high duplication rate across FFPE samples and achieve accurate gene expression estimates comparable to those from high quality material.  This would represent a significant step towards fully exploiting FFPE samples, particular those that would otherwise be discarded using standard approaches, increasing the opportunity to discover novel cancer drug targets and biomarkers from these samples. Read more about the challenge.


Background to the challenges

There has been a surge in interest in AI for Health and Care.  There are huge commercial opportunities in public health, vaccination, medicine discovery and manufacture, mental health services, medical technology for monitoring lifestyle and behaviour.   A key component to developing a successful and booming AI in health and care eco-system is providing tangible examples of the successful application of AI detailing the techniques, successes and limitations on real data.

The study group will fall under the broader umbrella of an Industrial Strategy Grand Challenge.  The Government’s Industrial Strategy sets out Grand Challenges to put the UK at the forefront of the industries of the future, ensuring that the UK takes advantage of major global changes, improving people’s lives and the country’s productivity.  The KTN study group will highlight how the application of mathematical sciences can contribute to the Industrial Strategy through helping to unlock key societal and economic challenges.

If you are a researcher working in a UK university who would like to work on these problems, please register here for the Manchester study group.

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