Abstract
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 language | English |
|---|---|
| Title of host publication | Methods in Molecular Biology |
| Pages | 169-182 |
| Number of pages | 14 |
| DOIs | |
| State | Published - 2021 |
Publication series
| Name | Methods in Molecular Biology |
|---|---|
| Volume | 2243 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Cancer detection
- Computational techniques
- Deep learning
- Genomic research
All Science Journal Classification (ASJC) codes
- Molecular Biology
- Genetics
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