Deep neural network: An efficient and optimized machine learning paradigm for reducing genome sequencing error

Ferdinand Kartriku, Robert Sowah, Charles Saah

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)27-30
Number of pages4
JournalInternational Journal of Engineering Trends and Technology
Volume68
Issue number9
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Deep learning
  • Error correction
  • Genome sequencing
  • Indels

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