IJBTT

Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error

	International Journal of Engineering Trends and Technology (IJETT)
	© 2020 by IJETT Journal
	Volume-68 Issue-9
	Year of Publication : 2020
	Authors : Dr. Ferdinand Kartriku, Robert Sowah, Charles Saah
	DOI :  10.14445/22315381/IJETT-V68I9P205

Citation 

MLA Style: Dr. Ferdinand Kartriku, Robert Sowah, Charles Saah  "Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error" International Journal of Engineering Trends and Technology 68.9(2020):27-30. 

APA Style:Dr. Ferdinand Kartriku, Robert Sowah, Charles Saah. Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error  International Journal of Engineering Trends and Technology, 68(9),27-30.

Abstract
Genomic data is used in many fields but, it has become known that most of the platforms used in the genome sequencing process produce significant errors. This means that the analysis and inferences generated from these data, may have some errors that need to be corrected. On the two main types (substitution and indels) of genome errors, our work focused on correcting errors emanating from indels. A deep learning approach was used to correct the errors in sequencing the chosen dataset.

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Keywords
genome sequencing; error correction; deep learning; indels;