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Details of Grant 

EPSRC Reference: EP/S003207/1
Title: Using Epigenetically-Inspired Connectionist Models to Provide Transparency In The Modelling of Human Visceral Leismaniasis
Principal Investigator: Turner, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: University of Hull
Scheme: New Investigator Award
Starts: 01 February 2019 Ends: 31 January 2020 Value (£): 90,691
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Jul 2018 EPSRC ICT Prioritisation Panel July 2018 Announced
Summary on Grant Application Form
Biologically inspired connectionist models are made up of multiple interconnected units which are designed to mimic biological processes in nature which give rise to emergent phenomena. Typically, connectionist models are used as computational tools which are capable of learning by example, for instance predicting the next days activity on the stock exchange by learning from previous months data. Epigenetically inspired connectionist models (EICMs) are a particular type of biologically inspired connectionist model which allow for the activation and deactivation of their interconnected units whist they are solving a task. These models have been shown to break complex tasks down into smaller sub-tasks autonomously, with certain interconnected units being applied to certain sub-tasks, and other interconnected units being applied to other sub-tasks.

Biologically inspired connectionist models in general are difficult to interpret. Their decision making processes are an emergent property of their interconnected units, from which it is very difficult to provide an explanation as to why specific decisions have been made. Because of this, deriving confidence from the decisions they make is difficult. Having confidence in the decision making process is of importance especially when the tasks they are applied to are in domains which are considered "high risk" such as medical simulations and financial forecasting.

To address these issues, this work aims to develop a set of techniques which allow for EICMs to provide a rationale for their decision making process, essentially making its decisions transparent. This will be achieved by analysing the way the model breaks down complex tasks, which of its units are active at any given time and then correlating this with the behaviour of both the network and the task.

We apply the EICMs and the techniques developed in this project to improve the understanding of the often fatal disease human visceral leismaniasis (HVL). The immune response to HVL is a significant indicator of patient outcome and is the product of the interplay between multiple interacting cells, macrophages and specific cytokine responses. The project partner Simomics, a world leading disease modelling company, has a comprehensive data set which describes changes to the immune response in reference to HVL over varying timescales, and has provided it for use during this project.

The overall development of HVL and the immune response to it is not well understood. The techniques developed in this work which are able to provide a rationale for their decision making process, will be applied to learn the interplay and interactions between these processes. This will allow for model to provide an explanation of what processes are most important in the immune response over the duration of HVL infection.

By contributing to the field of biological modelling, which places a strong emphasis on transparency and confidence in results, other fields will be able to adopt the models developed in this work to provide transparency in other domains.
Key Findings
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Organisation Website: http://www.hull.ac.uk