DocumentCode :
1686386
Title :
Inference of large-scale structural features of gene regulation networks using genetic algorithms
Author :
Nguyen, Viet Anh ; Zomaya, Albert Y.
Author_Institution :
Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW
fYear :
2008
Firstpage :
1
Lastpage :
8
Abstract :
Considerable attempts have been made to develop models and learning strategies to infer gene networks starting from single connections. However, due to noise and other difficulties that arise from making measurements at the meso and nano levels, these so called bottom-up approaches have not been of much success. The need for methods that use a top-down approach to extract global statistics from expression data has emerged to deal with such difficulties. This paper presents a theoretical framework that employs global statistics learnt from gene expression data to infer different network structural properties of large- scale gene regulatory networks. The framework is inspired by genetic algorithms and designed with the aim to address the different weaknesses in existing approaches. Experimental results show that the developed system is more superior to previously published results.
Keywords :
biology computing; genetic algorithms; gene regulation networks; genetic algorithms; large-scale structural features; learning strategies; Biological system modeling; Biomedical measurements; Fungi; Gene expression; Genetic algorithms; Large-scale systems; Network topology; Noise level; Noise measurement; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on
Conference_Location :
Miami, FL
ISSN :
1530-2075
Print_ISBN :
978-1-4244-1693-6
Electronic_ISBN :
1530-2075
Type :
conf
DOI :
10.1109/IPDPS.2008.4536370
Filename :
4536370
Link To Document :
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