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Biological Network Approaches and Applications in Rare Disease Studies
Open AccessReview

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

1
Portuguese Association for CDG, 2820-381 Lisboa, Portugal
2
CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516, Lisboa, Portugal
3
UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
4
Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
5
Departamento de Ciências e Tecnologias, Autónoma Techlab–Universidade Autónoma de Lisboa, 1169-023 Lisboa, Portugal
6
Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, 1959-007 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Authors contributed equally.
Genes 2019, 10(12), 978; https://doi.org/10.3390/genes10120978
Received: 30 September 2019 / Revised: 19 November 2019 / Accepted: 20 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue Bioinformatic Analysis for Rare Diseases)
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.
Keywords: artificial intelligence; big data; congenital disorders of glycosylation; diagnosis; drug repurposing; machine learning; personalized medicine; rare diseases artificial intelligence; big data; congenital disorders of glycosylation; diagnosis; drug repurposing; machine learning; personalized medicine; rare diseases
MDPI and ACS Style

Brasil, S.; Pascoal, C.; Francisco, R.; dos Reis Ferreira, V.; Videira, P.A.; Valadão, G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter? Genes 2019, 10, 978.

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