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

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

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

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

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Abstract

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 language	English
Title of host publication	In: Proceedings IEEE Intelligent Systems 2004
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	150-155
Volume	1
Publication status	Published - 2004

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title = "Optimising a hierarchical neural clusterer applied to large gene sequence data sets",

abstract = "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.",

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AU - Adams, Roderick

AU - Davey, N.

AU - Kaye, Paul H.

AU - Pensuwon, W.

N1 - Original article can be found at: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=9314

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

M3 - Conference contribution

VL - 1

SP - 150

EP - 155

BT - In: Proceedings IEEE Intelligent Systems 2004

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

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

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