Year: 2017 | Month: December | Volume 5 | Issue 2
Computational Machine Learning Application on Microarray Genomic Data
DOI:10.5958/2321-7111.2017.00007.5
Abstract:
Genome Analysis of a human being permits useful insight into the ancestry of that person and also facilitates the determination of weaknesses and susceptibilities of that person towards inherited diseases. The amount of accumulated genome data is increasing at a tremendous rate with the rapid development of genome sequencing technologies and gene prediction is one of the most challenging tasks in genome analysis. Many tools have been developed for gene prediction which still remains as an active research area. Gene prediction involves the analysis of the entire genomic data that is accumulated in the database and hence scrutinizing the predicted genes takes too much of time. However, the computational time can be reduced and the process can be made more effective through the selection of dominant genes. In this paper, a novel method is presented to predict the dominant genes of ALL/AML cancer. First, to train an FF-ANN a combinational data of the input dataset is generated and its dimensionality is reduced through Probability Principal Component Analysis (PPCA). Then, the classified database of ALL/AML cancer is given as the training dataset to design the FF-ANN. After the FF-ANN is designed, the genetic algorithm is applied on the test input sequence and the fitness function is computed using the designed FF-ANN. After that, the genetic operations crossover, mutation and selection are carried out. Finally, through analysis, the optimal dominant genes are predicted.
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