A Phenomic Approach to Genetic Algorithms for Reconstruction of Gene Networks

D’Souza, Rio; Sekaran, K. Chandra; Kandasamy, A.

doi:10.1007/978-3-642-14834-7_19

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Cited by 2 publications

(3 citation statements)

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“…It is possible to model various levels of biological activity from gene regulation and protein interaction to metabolic and biochemical reaction [29]. Figure 1 shows a simplified two gene system which models the gene and protein interaction levels only.…”

Section: Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Reconstructing biological gene regulatory networks: where optimization meets big data

Thomas

Jin

2013

Evol. Intel.

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Section: Modelsmentioning

confidence: 99%

“…This method has been used by several researchers [29,47,120], however this adds the need for a clustering process and averaging expression profiles before the optimization stage creating additional overhead and potential errors. Altough this techniques does fit with some biological observations, i.e.…”

Section: Dealing With Datamentioning

confidence: 99%

Reconstructing biological gene regulatory networks: where optimization meets big data

Thomas

Jin

2013

Evol. Intel.

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“…Currently, microarray datasets are broadly used for this purpose. By employing their algorithm on a yeast speculation dataset, D'Souza and colleagues 21 have demonstrated that their algorithm can detect gene networks with reasonable ease. A firm gene selection and efficient cancer prediction structure called SGS has been introduced.…”

Section: Review Of Researchmentioning

confidence: 99%

A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering

Rajadurai

Sankaranarayanan

2012

Bioinform Biol Insights

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Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.

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A Phenomic Approach to Genetic Algorithms for Reconstruction of Gene Networks

Cited by 2 publications

References 15 publications

Reconstructing biological gene regulatory networks: where optimization meets big data

Reconstructing biological gene regulatory networks: where optimization meets big data

A Fast Quad-Tree Based Two Dimensional Hierarchical Clustering

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