Artificial intelligence and database for NGS-based diagnosis in rare disease
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1258083/full |
| _version_ | 1797333531089698816 |
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| author | Yee Wen Choon Yee Wen Choon Yee Fan Choon Nurul Athirah Nasarudin Fatma Al Jasmi Muhamad Akmal Remli Muhamad Akmal Remli Mohammed Hassan Alkayali Mohd Saberi Mohamad |
| author_facet | Yee Wen Choon Yee Wen Choon Yee Fan Choon Nurul Athirah Nasarudin Fatma Al Jasmi Muhamad Akmal Remli Muhamad Akmal Remli Mohammed Hassan Alkayali Mohd Saberi Mohamad |
| author_sort | Yee Wen Choon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T08:06:19Z |
| format | Article |
| id | doaj.art-2abf10cc48bd41f28228ebf2e4b734f4 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-08T08:06:19Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-2abf10cc48bd41f28228ebf2e4b734f42024-02-02T10:36:22ZengFrontiers Media S.A.Frontiers in Genetics1664-80212024-01-011410.3389/fgene.2023.12580831258083Artificial intelligence and database for NGS-based diagnosis in rare diseaseYee Wen Choon0Yee Wen Choon1Yee Fan Choon2Nurul Athirah Nasarudin3Fatma Al Jasmi4Muhamad Akmal Remli5Muhamad Akmal Remli6Mohammed Hassan Alkayali7Mohd Saberi Mohamad8Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, MalaysiaFaculty of Data Science and Informatics, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, MalaysiaFaculty of Dentistry, Lincoln University College, Petaling Jaya, Selangor, MalaysiaHealth Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesHealth Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesInstitute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, MalaysiaFaculty of Data Science and Informatics, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, MalaysiaSchool of Postgraduate Studies, United Arab Emirates University, Al Ain, United Arab EmiratesHealth Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesRare 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.https://www.frontiersin.org/articles/10.3389/fgene.2023.1258083/fullrare diseasediagnosisnext-generation sequencingartificial intelligencemachine learningdata science |
| spellingShingle | Yee Wen Choon Yee Wen Choon Yee Fan Choon Nurul Athirah Nasarudin Fatma Al Jasmi Muhamad Akmal Remli Muhamad Akmal Remli Mohammed Hassan Alkayali Mohd Saberi Mohamad Artificial intelligence and database for NGS-based diagnosis in rare disease Frontiers in Genetics rare disease diagnosis next-generation sequencing artificial intelligence machine learning data science |
| title | Artificial intelligence and database for NGS-based diagnosis in rare disease |
| title_full | Artificial intelligence and database for NGS-based diagnosis in rare disease |
| title_fullStr | Artificial intelligence and database for NGS-based diagnosis in rare disease |
| title_full_unstemmed | Artificial intelligence and database for NGS-based diagnosis in rare disease |
| title_short | Artificial intelligence and database for NGS-based diagnosis in rare disease |
| title_sort | artificial intelligence and database for ngs based diagnosis in rare disease |
| topic | rare disease diagnosis next-generation sequencing artificial intelligence machine learning data science |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1258083/full |
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