Towards evaluation of inferred gene network

Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organism...

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Main Authors: Zainudin, S., Deris, S.
Format: Book Section
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2007
Subjects:
Online Access:http://eprints.utm.my/9642/
http://eprints.utm.my/9642/1/SafaaiDeris2007_TowardsEvaluationOfInferredGene.pdf
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author Zainudin, S.
Deris, S.
author_facet Zainudin, S.
Deris, S.
author_sort Zainudin, S.
building UTeM Institutional Repository
collection Online Access
description Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S. cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.
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spelling utm-96422017-08-15T01:37:00Z http://eprints.utm.my/9642/ Towards evaluation of inferred gene network Zainudin, S. Deris, S. QA75 Electronic computers. Computer science Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S. cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions. Institute of Electrical and Electronics Engineering (IEEE) 2007-08 Book Section PeerReviewed application/pdf en http://eprints.utm.my/9642/1/SafaaiDeris2007_TowardsEvaluationOfInferredGene.pdf Zainudin, S. and Deris, S. (2007) Towards evaluation of inferred gene network. In: International Conference on Computational Science and its Applications ICCSA 2007. Institute of Electrical and Electronics Engineering (IEEE), pp. 57-64. ISBN 978-0-7695-2945-5 http://dx.doi.org/10.1109/ICCSA.2007.29 doi : 10.1109/ICCSA.2007.29
spellingShingle QA75 Electronic computers. Computer science
Zainudin, S.
Deris, S.
Towards evaluation of inferred gene network
title Towards evaluation of inferred gene network
title_full Towards evaluation of inferred gene network
title_fullStr Towards evaluation of inferred gene network
title_full_unstemmed Towards evaluation of inferred gene network
title_short Towards evaluation of inferred gene network
title_sort towards evaluation of inferred gene network
topic QA75 Electronic computers. Computer science
url http://eprints.utm.my/9642/
http://eprints.utm.my/9642/
http://eprints.utm.my/9642/
http://eprints.utm.my/9642/1/SafaaiDeris2007_TowardsEvaluationOfInferredGene.pdf