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-List Of Titles -Using dynamic Bayesian networks to infer gene regulatory networks from expression profiles

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/149812

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Title
Using dynamic Bayesian networks to infer gene regulatory networks from expression profiles
Related
ACM Symposium on Applied Computing (24th : 2009) (8 - 12 March 2009 : Honolulu)
Related
Shin, Sung Y. and Ossowski, Sascha. Proceedings of the 2009 ACM Symposium on Applied Computing : 2009, Honolulu, Hawaii, p.799-803
DOI
10.1145/1529282.1529449
Publisher
New York, N.Y : Association for Computing Machinery (ACM)
Date
2009
FoR/RFCD Code(s)
060102 Bioinformatics
Author/Creator
Shermin, Akther
Author/Creator
Orgun, Mehmet A
Description
Two major challenges in inferring the sparse topological architecture of Gene Regulatory Networks using computational methods are 1) the low accuracy of predicting connections between genes and 2) the excessive computational cost. In order to address these challenges, we have exploited some biological features of yeast cell cycle. One such feature is that, a high proportion of Cell Cycle Regulated genes are periodically expressed; that is genes are maximally expressed to affect and control the regulation of other genes and on completing certain tasks; they are repressed by some other regulator genes. Thus the whole cell cycle progresses systematically through the successive activation and inactivation of CCR genes. To use this feature, we have calculated the peak time of individual genes which falls into one/more phases of the cell cycle. Therefore, genes that peak in the interval of the same phase of the cell cycle have been grouped together. Finally, we have applied the Dynamic Bayesian Network (DBN) algorithm within distinct phases of genes. As a consequence, both the accuracy and the computational cost of our learning algorithm have been improved in comparison with the existing DBN algorithms.
Description
5 page(s)
Subject Keyword
060102 Bioinformatics
Subject Keyword
Algorithms
Subject Keyword
Performance
Subject Keyword
Experimentation
Subject Keyword
Verification
Resource Type
conference paper
Organisation
Macquarie University. Dept. of Computing

Identifier
http://hdl.handle.net/1959.14/149812
Identifier
ISBN:9781605581668
Identifier
mq-rm-2009005336
Language
eng
Reviewed
Reviewed
Save/E-mail Citation
Citation Format
E-mail Address
Subject
"Proceedings of the 2009 ACM Symposium on Applied Computing : 2009, Honolulu, Hawaii"
 
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