Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error
This addresses sequencing errors for genomic data analysis, but appears incremental as it applies existing deep learning methods to a specific error type.
The paper tackled the problem of indel errors in genome sequencing by applying a deep learning approach to correct them, though no concrete results or numbers were provided.
Genomic data I used in many fields but, it has become known that most of the platforms used in the 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 of genome errors - substitution and indels - our work is focused on correcting indels. A deep learning approach was used to correct the errors in sequencing the chosen dataset