Ranking of Cancer Mediating Genes: A Novel Approach Using Genetic Algorithm in DNA Microarray Gene Expression Dataset

2018 ◽  
pp. 129-137 ◽  
Author(s):  
Sujay Saha ◽  
Priyojit Das ◽  
Anupam Ghosh ◽  
Kashi Nath Dey
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Dna Microarray ◽  
Gene Expression Dataset ◽  
Microarray Gene Expression ◽  
Novel Approach ◽  
Microarray Gene

Local Optima Avoidance in GA Biclustering using Map Reduce

2016 ◽  
Vol 6 (1) ◽  
pp. 37-47
Author(s):  
Gowri R. ◽  
Rathipriya R.
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Population Size ◽  
Optimal Solution ◽  
Search Space ◽  
Gene Expression Dataset ◽  
Map Reduce ◽  
Mapreduce Framework ◽  
Local Optima ◽  
Novel Approach

One of the prominent issues in Genetic Algorithm (GA) is premature convergence on local optima. This restricts the enhanced optimal solution searching in the entire search space. Population size is one of the influencing factors in Genetic Algorithm. Increasing the population size will improvise the randomized searching and maintains the diversity in the population. It also increases its computational complexity. Especially in GA Biclustering (GABiC), the search should be randomized to find more optimal patterns. In this paper, a novel approach for population setup in MapReduce framework is proposed. The maximal population is split into population sets, and these groups will proceed searching in parallel using MapReduce framework. This approach is attempted for biclustering the gene expression dataset in this paper. The performance of this proposed work seems promising on comparing its results with those obtained from previous hybridized optimization approaches. This approach will also handle data scalability issues and applicable to the big data biclustering problems.

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