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 language | English |
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Pages (from-to) | 27-30 |
Number of pages | 4 |
Journal | International Journal of Engineering Trends and Technology |
Volume | 68 |
Issue number | 9 |
DOIs | |
Publication status | Published - Sep 2020 |
Keywords
- Deep learning
- Error correction
- Genome sequencing
- Indels