MultiNNProm: A multi-classifier system for finding genes

Romesh Ranawana, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


The computational identification of genes in DNA sequences has become an issue of crucial importance due to the large number of DNA molecules being currently sequenced. We present a novel neural network based multi-classifier system, MultiNNProm, for the identification of promoter regions in E. Coli1 DNA sequences. The DNA sequences were encoded using four different encoding methods and were used to train four different neural networks. The classification results of these neural networks were then aggregated using a variation of the LOP method. The aggregating weights used within the modified LOP aggregating algorithm were obtained through a genetic algorithm. We show that the use of different neural networks, trained on the same set of data, could provide slightly varying results if the data were differently encoded. We also show that the combination of more neural classifiers provides us with better accuracy than the individual networks.

Original languageEnglish
Title of host publicationApplied Soft Computing Technologies: The Challenge of Complexity
EditorsAjith Abraham, Bernard de Baets, Mario Koppen, Bertram Nickolay
PublisherSpringer Verlag
Number of pages13
ISBN (Print)3540316493, 9783540316497
Publication statusPublished - 2006
Externally publishedYes
Event9th Online World Conference on Soft Computing in Industrial Applications (WSC9) -
Duration: 20 Sept 20048 Oct 2004
Conference number: 9

Publication series

NameAdvances in Soft Computing
ISSN (Print)16153871
ISSN (Electronic)18600794


Conference9th Online World Conference on Soft Computing in Industrial Applications (WSC9)

Bibliographical note

This paper is not available in Pure.

ASJC Scopus subject areas

  • Computational Mechanics
  • Computer Science Applications
  • Computer Science (miscellaneous)


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