A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

ABSTRACT: Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or var

SUBMITTER: Pratella D 

PROVIDER: S-EPMC8509321 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature