Deep Learning Applied on Next Generation Sequencing Data Analysis

Artem Danilevsky, Noam Shomron

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Deep learning is defined as the group of computational techniques allowing for the discovery of latent information within large amounts of data. Recently, many fields have seen the immense potential of deep learning to solve various tasks in ways which outperformed many other traditional methods. Genomic research could be the next frontier to take advantage of deep learning, as it has the perfect combination of vast amounts of data and diverse tasks. Here we present the platform we generated to combine deep learning and genomic sequencing data. We tested the platform on publicly available sequencing data from the gut microbiome of cancer patients. We showed that our platform is capable of classifying patients with higher accuracy than other methods, with some caveats. Overall, we believe genomic research is the next frontline for deep learning as there are exciting avenues waiting to be explored. We think that our platform, presented here, could serve as the basis for such future research.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
Number of pages14
StatePublished - 2021

Publication series

NameMethods in Molecular Biology


  • Cancer detection
  • Computational techniques
  • Deep learning
  • Genomic research

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics


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