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

被引:76
作者
Brasil, Sandra [1 ,2 ]
Pascoal, Carlota [1 ,2 ,3 ]
Francisco, Rita [1 ,2 ,3 ]
Ferreira, Vanessa dos Reis [1 ,2 ]
Videira, Paula A. [1 ,2 ,3 ]
Valadao, Goncalo [4 ,5 ,6 ]
机构
[1] Portuguese Assoc CDG, P-2820381 Lisbon, Portugal
[2] Univ Nova Lisboa, Fac Ciencias & Tecnol, CDG & Allies PPAIN, P-2829516 Lisbon, Portugal
[3] Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Ciencias Vida, UCIBIO, P-2829516 Lisbon, Portugal
[4] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal
[5] Univ Autonoma Lisboa, Autonoma Techlab, Dept Ciencias & Tecnol, P-1169023 Lisbon, Portugal
[6] Inst Super Engn Lisboa, Elect Telecommun & Comp Engn Dept, P-1959007 Lisbon, Portugal
关键词
artificial intelligence; big data; congenital disorders of glycosylation; diagnosis; drug repurposing; machine learning; personalized medicine; rare diseases; FACIAL DYSMORPHOLOGY; DATA WAREHOUSE; PHENOTYPE; CLASSIFICATION; GLYCOSYLATION; RECOGNITION; PREDICTION; DISORDERS; FRAMEWORK; VARIANTS;
D O I
10.3390/genes10120978
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
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页数:24
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