Evolutionary ensemble for in Silico prediction of ames test mutagenicity

Huanhuan CHEN, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


Driven by new regulations and animal welfare, the need to develop in silica models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals. © Springer-Verlag Berlin Heidelberg 2007.
Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications : Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings
EditorsDe-Shuang HUANG, Laurent HEUTTE, Marco LOOG
PublisherSpringer Berlin Heidelberg
Number of pages10
ISBN (Electronic)9783540742050
ISBN (Print)9783540742012
Publication statusPublished - 2007
Externally publishedYes
Event3rd International Conference on Intelligent Computing, ICIC 2007 - Qingdao, China
Duration: 21 Aug 200724 Aug 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Intelligent Computing, ICIC 2007


  • Ames test mutagenicity
  • Evolutionary ensemble
  • In silico models
  • Negative correlation learning


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