Optimising a hierarchical neural clusterer applied to large gene sequence data sets

Roderick Adams, N. Davey, Paul H. Kaye, W. Pensuwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Downloads (Pure)


Evolutionary Algorithms have been used to optimise the performance of neural network models before. This paper uses a hybrid approach by permanently attaching a Genetic Algorithm (GA) to a hierarchical clusterer to investigate appropriate parameter values for producing specific tree shaped representations for some gene sequence data. It addresses a particular problem where the size of the data set makes the direct use of a GA too time consuming. We show by using a data set nearly two orders of magnitude smaller in the GA investigation that the results can be usefully translated across to the real, much larger data sets. The data
sets in question are gene sequences and the aim of the analysis was to cluster short sub-sequences that could represent binding sites that regulate the expression of genes.
Original languageEnglish
Title of host publicationIn: Proceedings IEEE Intelligent Systems 2004
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication statusPublished - 2004


Dive into the research topics of 'Optimising a hierarchical neural clusterer applied to large gene sequence data sets'. Together they form a unique fingerprint.

Cite this