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Immune Inspired Memory Algorithms Applied to Unknown Motif Detection
 

Summary: Immune Inspired Memory Algorithms Applied to Unknown
Motif Detection
William Owen Wilson
Thesis submitted to the University of Nottingham
for the degree of Doctor of Philosophy
July 2008
Abstract
This thesis investigates the application of principles inspired from the develop-
ment of immune memory to the problem of unknown motif detection in time se-
ries data. Motifs represent repeating patterns in the underlying data. As human
beings we naturally seek patterns or motifs in data in order to understand that
information. Motifs indicate high level properties of the data, summarising the
information in a compact, intuitive and meaningful manner. Motifs help identify
relationships in the data and they can aid in the process of prediction and fore-
casting. The discovery of previously unknown motifs therefore has considerable
value.
Studying the evolution of naive immune cells in their path to becoming mem-
ory provides a valuable insight into the way the immune system learns and adapts
to recognise and remember the information it encounters. Memory cells repre-
sent the solutions that the immune system has generated and wishes to remember.

  

Source: Aickelin, Uwe - School of Computer Science, University of Nottingham

 

Collections: Computer Technologies and Information Sciences