Artificial intelligence and database for NGS-based diagnosis in rare disease

被引:13
作者
Choon, Yee Wen [1 ,2 ]
Choon, Yee Fan [3 ]
Nasarudin, Nurul Athirah [4 ]
Al Jasmi, Fatma [4 ]
Remli, Muhamad Akmal [1 ,2 ]
Alkayali, Mohammed Hassan [5 ]
Mohamad, Mohd Saberi [4 ]
机构
[1] Univ Malaysia Kelantan, Inst Artificial Intelligence & Big Data, Kota Baharu, Kelantan, Malaysia
[2] Univ Malaysia Kelantan, Fac Data Sci & Informat, Kota Baharu, Kelantan, Malaysia
[3] Lincoln Univ Coll, Fac Dent, Petaling Jaya, Selangor, Malaysia
[4] United Arab Emirates Univ, Coll Med & Hlth Sci, Dept Genet & Genom, Hlth Data Sci Lab, Al Ain, U Arab Emirates
[5] United Arab Emirates Univ, Sch Postgrad Studies, Al Ain, U Arab Emirates
关键词
rare disease; diagnosis; next-generation sequencing; artificial intelligence; machine learning; data science; GENETIC-DISORDERS; INHERITANCE; VARIANTS; PANEL;
D O I
10.3389/fgene.2023.1258083
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.
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页数:11
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