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The University of Southampton
Institute for Life Sciences

Machine learning and the Life Sciences

Our scientists, engineers, mathematicians are collaboratively developing and using computational methods and techniques such as artificial & augmented intelligence, machine learning and simulations to analyse large complex datasets and apply the information and knowledge generated to real world challenges.

Image credit: Prof Syma Khalid

New technologies have facilitated the creation and collection of large and complex datasets relating to life sciences research. For example, those derived from molecular and cellular data such as genomics, proteomics and metabolomics; biological and medical imaging and population level data are transforming our understanding of the complex environmental, social and molecular challenges.  

However, the datasets themselves are only part of the story, it is an increasing challenge to extract the right information and make it applicable to the major societal and global challenges that need solving.

AI Discovery Cycle

From challenges like migration to antibiotic resistance to stem cell therapy, scientists from across the University in areas such as Medicine, Computer Science, Chemistry and Mathematics are coming together and using new interdisciplinary approaches and techniques such as Artificial Intelligence and Machine Learning to extract important information and derive knowledge from these datasets, that will lead both to improved understanding of the issues and problems and enable new approaches for prevention and provide potential solutions.

Related Staff Member

Key words

AI, Artificial Intelligence, Augmented Intelligence, Biology, Chemistry, Complex Datasets, Data, Interdisciplinary, Life Sciences, Machine Learning, ML, Mathematical and Computational Methods, Networks, Physics, Simulation

Please see a selection of postgraduate courses related to this subject area below. 


For the full range of undergraduate and postgraduate courses at the University of Southampton, please visit our courses webpages https://www.southampton.ac.uk/courses.page

MSc Data Science

This programme builds core areas of expertise, from operating high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.

MSc Computer Science

This one year degree incudes modules from our more specialist programmes including artificial intelligence, cyber security, signal processing, software engineering, and web science and technology.

MSc System On Chip

System on chip design techniques, modules on nanoelectronic devices, digital system design & electronic design automation. Optional modules include medical electronic technologies.

MSc Systems, Control and Signal Processing

This MSc Systems, Control and Signal Processing degree includes core topics of signal processing with specialisms in systems theory, image processing and machine learning.

MSc Artificial Intelligence

This one year degree offers wide-ranging options including intelligent agents, complexity science, computer vision, robotics and machine learning techniques.

Artificial Intelligence, Augmented Intelligence for Automated Investigations for Scientific Discovery (AI3SD) Network

It is now widely accepted that almost all science now depends on computational assistance.

Southampton scientists in the areas of chemistry and materials will lead a new network of researchers from across the UK that aims to create an environment in which leading AI developments can be applied to the chemical and materials discovery.

Funded by the UKRI/ESPRC, the Artificial Intelligence, Augmented Intelligence for Automated Investigations for Scientific Discovery (AI3SD) Network brings together dedicated researchers to show how cutting edge artificial and augmented intelligence technologies can be used to push the boundaries of scientific discovery. It involves and collaborates with academics, commercial organizations and government officials.

The work will focus on Design and Synthesis of Chemicals and Materials (including property prediction, synthesis and manufacture). The complexity of the relationships between chemical and molecular structure, physical properties and material performance renders many of these problems intractable by conventional approaches to computation.  AI techniques hold great promise in revolutionising research in these areas. These areas are also critical to meeting the majority of the UN sustainability goals and are at the top of the UK Industrial Strategy.

www.ai3sd.ac.uk

Contacts: Prof Jeremy Frey, Prof Niranjan

 

Human migration

The movement of people across borders is something we hear about every day. Some movements go largely unnoticed, but other provoke debate. However, all movements have an impact on society. International migration is one of the most uncertain components of population change.

Our researchers, in social sciences, maths and psychology, are working to change the way in which migration can be understood, predicted, and managed by effectively integrating behavioural and social theory with modelling.

The Bayesian Agent-Based Population Studies (BAPS) project aims to develop a ground-breaking simulation model of international migration, based on a population of intelligent, cognitive agents, their social networks and institutions, all interacting with one another. 

The simulations will see the agents (migrants, states and institutions) interacting in a way that will mimic the trends observed in the real world. State-of-the-art statistical tools will identify the exact areas for experimentation, and cognitive experiments will help improve understanding about the way people make migration decisions under uncertainty.

The project will allow our teams to evaluate various migration policies through computer simulations and experiments under controlled, yet realistic conditions and will offer a pioneering environment for applying the findings in practice.

Contact: Prof Jakub Bijak

Using computational chemistry to prevent antibiotic resistance

Antimicrobial resistance is one of the world’s most pressing challenges and if something is not done, by 2050 infections and illnesses that would previously have been curable by antibiotics will kill more people worldwide than cancer.

Scientists across the University are working tirelessly to find new ways to combat bacterial resistance to antibiotics. In a project funded by UKRI/BBSRC our teams are bringing computer graphics and coding together with biology and chemistry through computational chemistry to delve into the structure and processes within the membranes that surround bacterial cells.

The team employs a range of computational methods supported by electrophysiology experiments by our collaborators in Newcastle, to explain the molecular pathways taken by bacteria to enter cells through a specific protein called the OprD protein. This protein is one member of the largest family of proteins located within the outer membrane of Pseudomonas aeruginosa bacteria, a common human, animal and plant pathogen that is resistant to many different antibiotics, and is one of the so-called ‘super bugs’. Studying this specific protein and bacterium offers our researchers an excellent opportunity to examine exactly how some proteins allow antibiotics to enter bacteria, and how we might be able to harness these proteins in order to create new, successful drugs.

Computational chemistry methods use computer simulation alongside theoretical chemistry to solve problems, predict outcomes, and complement experiments taking place in laboratories. Using these methods enables researchers to predict and rationalise phenomena at time and length scales that are difficult to achieve with experimental methods. When simulations are combined with experimental data; the multidisciplinary approach enables scientists to achieve greater depths of understanding.

Contact: Prof Syma Khalid

A molecular model of both membranes, proteins and the cell wall of a Gram-negative bacterium from the Khalid group
Molecular Model credit: Prof Syma Khalid
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