Gene network inference using biological homogeneity index based-clustering and constraint-based searching

Gene network inference involves exploration of an exponential search space. Initially, network inference utilizes microarray data as a single data source. However, due to microarray data limitations, other biological data is combined with microarray data for network inference. Previous research has...

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Main Author:	Zainudin, Suhaila
Format:	Thesis
Published:	2010
Subjects:	QA75 Electronic computers Computer science
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spelling	my-utm-ep.267622017-08-23T03:50:35Z Gene network inference using biological homogeneity index based-clustering and constraint-based searching 2010 Zainudin, Suhaila QA75 Electronic computers. Computer science Gene network inference involves exploration of an exponential search space. Initially, network inference utilizes microarray data as a single data source. However, due to microarray data limitations, other biological data is combined with microarray data for network inference. Previous research has produced biological homogeneity measures based on functional annotations from Gene Ontology for various clustering algorithms. Biological homogeneity measures the ability of a clustering algorithm to produce biologically meaningful clusters. Biological Homogeneity Index (BHI) is measured for a range of fc values for fc-means clustering algorithm to find clusters which score the highest homogeneity index. Results are compared using whole dataset, fc-means clusters and fc-means clusters with BHI (fc-means /BHI) approaches. Experimental results have shown that the fc-means clusters produced statistically significant valid number of gene interactions compared to the whole dataset. In comparing the fc-means clusters and fc-means /BHI clusters, the fc-means /BHI clusters produces more valid number of gene interactions for all experiments. Statistical significance test results show that these improvements are too small to be statistically significant. Hence, biological enrichment scores are also used for evaluation. Enrichment scores for fc-means /BHI clusters are better than scores for fc-means clusters. This research employs the constraint-based search algorithm called Grow-Shrink algorithm (GS) in learning the best network structure. Experiments are performed to compare the performance for constraint-based search against scorebased approaches such as the Greedy Search (GRS) and Simulated Annealing (SA). Experimental results prove that GS performs better than GRS and SA in terms of valid interactions number. However, the improvements are too small to be statistically significant. The thesis concludes that using prior biological knowledge can help form biologically meaningful clusters. Using constraint-based search algorithm is also useful for improving the quality of gene network inference. 2010 Thesis http://eprints.utm.my/id/eprint/26762/ http://libraryopac.utm.my/client/en_AU/main/search/results?qu=Gene+network+inference+using+biological+homogeneity+index+based-clustering+and+constraint-based+searching&te= phd doctoral Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Zainudin, Suhaila Gene network inference using biological homogeneity index based-clustering and constraint-based searching
description	Gene network inference involves exploration of an exponential search space. Initially, network inference utilizes microarray data as a single data source. However, due to microarray data limitations, other biological data is combined with microarray data for network inference. Previous research has produced biological homogeneity measures based on functional annotations from Gene Ontology for various clustering algorithms. Biological homogeneity measures the ability of a clustering algorithm to produce biologically meaningful clusters. Biological Homogeneity Index (BHI) is measured for a range of fc values for fc-means clustering algorithm to find clusters which score the highest homogeneity index. Results are compared using whole dataset, fc-means clusters and fc-means clusters with BHI (fc-means /BHI) approaches. Experimental results have shown that the fc-means clusters produced statistically significant valid number of gene interactions compared to the whole dataset. In comparing the fc-means clusters and fc-means /BHI clusters, the fc-means /BHI clusters produces more valid number of gene interactions for all experiments. Statistical significance test results show that these improvements are too small to be statistically significant. Hence, biological enrichment scores are also used for evaluation. Enrichment scores for fc-means /BHI clusters are better than scores for fc-means clusters. This research employs the constraint-based search algorithm called Grow-Shrink algorithm (GS) in learning the best network structure. Experiments are performed to compare the performance for constraint-based search against scorebased approaches such as the Greedy Search (GRS) and Simulated Annealing (SA). Experimental results prove that GS performs better than GRS and SA in terms of valid interactions number. However, the improvements are too small to be statistically significant. The thesis concludes that using prior biological knowledge can help form biologically meaningful clusters. Using constraint-based search algorithm is also useful for improving the quality of gene network inference.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zainudin, Suhaila
author_facet	Zainudin, Suhaila
author_sort	Zainudin, Suhaila
title	Gene network inference using biological homogeneity index based-clustering and constraint-based searching
title_short	Gene network inference using biological homogeneity index based-clustering and constraint-based searching
title_full	Gene network inference using biological homogeneity index based-clustering and constraint-based searching
title_fullStr	Gene network inference using biological homogeneity index based-clustering and constraint-based searching
title_full_unstemmed	Gene network inference using biological homogeneity index based-clustering and constraint-based searching
title_sort	gene network inference using biological homogeneity index based-clustering and constraint-based searching
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2010
_version_	1747815504698933248

Gene network inference using biological homogeneity index based-clustering and constraint-based searching

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