Practical applications of GANs in genomics
GANs have found a lot of applications in several domains such as NLP, CV, and genomics because of their ability to produce synthetic data samples to augment the real world and help improve models’ fitness. State-of-the-art synthetic models such as GANs can produce an artificial version of high-dimensional and complex genomic datasets with high accuracy, scale, and privacy. The artificial datasets can be shared among researchers and enable future genomics research and safe, private data sharing between researchers, health care providers, and the industry. As discussed briefly in the introduction, there are several use cases of GANs in genomics such as the automatic design of probe sequences for binding assays, optimization of genomic sequences, creation of synthetic genomes, and so on.
We will now see some examples of how GANs are applied to genomics and solve some real-world problems in the following section.
