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REVIEW article

Front. Genet.
Sec. Computational Genomics
Volume 14 - 2023 | doi: 10.3389/fgene.2023.1258083
This article is part of the Research Topic

AI and Multi-Omics for Rare Diseases: Challenges, Advances and Perspectives, Volume III

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Artificial Intelligence and Database for NGS-Based Diagnosis in Rare Disease

Yee Wen Choon1, 2 Yee Fan Choon3 Nurul Athirah Nasarudin4 Muhammad Akmal Remli1, 2 Mohammed Hassan Alkayali5 Mohd Saberi Mohamad4*
  • 1Faculty of Data Science and Informatics, Universiti Malaysia Kelantan, Malaysia
  • 2Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan,, Malaysia
  • 3Faculty of Dentistry, Lincoln University College, Malaysia
  • 4Health Data Science Lab, College of Medicine and Health Sciences, United Arab Emirates University, United Arab Emirates
  • 5School of Postgraduate Studies, United Arab Emirates University, United Arab Emirates

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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 will compare several rare disease databases.

Keywords: rare disease, diagnosis, Next-generation sequencing, artificial intelligence, machine learning, Data Science Font: (Default) +Headings CS (Times New Roman), Complex Script Font: +Headings CS (Times New Roman)

Received: 03 Aug 2023; Accepted: 24 Nov 2023.

Copyright: © 2023 Choon, Choon, Nasarudin, Remli, Alkayali and Mohamad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Mohd Saberi Mohamad, Health Data Science Lab, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, 15551, United Arab Emirates