How data science and AI-based technologies impact genomics
Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer – Medknow Publications
2023-01-01
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| Series: | Singapore Medical Journal |
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| Online Access: | https://journals.lww.com/10.4103/singaporemedj.SMJ-2021-438 |
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| _version_ | 1823861516027297792 |
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| author | Jing Lin Kee Yuan Ngiam |
| author_facet | Jing Lin Kee Yuan Ngiam |
| author_sort | Jing Lin |
| collection | DOAJ |
| description | Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies. |
| format | Article |
| id | doaj-art-f329d78b92ab40cb8a3a3f2bd669f9cd |
| institution | Kabale University |
| issn | 0037-5675 2737-5935 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wolters Kluwer – Medknow Publications |
| record_format | Article |
| series | Singapore Medical Journal |
| spelling | doaj-art-f329d78b92ab40cb8a3a3f2bd669f9cd2025-02-09T13:25:09ZengWolters Kluwer – Medknow PublicationsSingapore Medical Journal0037-56752737-59352023-01-01641596610.4103/singaporemedj.SMJ-2021-438How data science and AI-based technologies impact genomicsJing LinKee Yuan NgiamAdvancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.https://journals.lww.com/10.4103/singaporemedj.SMJ-2021-438artificial intelligencedeep learninggenome-wide association studypharmacogenomicsphenome-wide association study |
| spellingShingle | Jing Lin Kee Yuan Ngiam How data science and AI-based technologies impact genomics Singapore Medical Journal artificial intelligence deep learning genome-wide association study pharmacogenomics phenome-wide association study |
| title | How data science and AI-based technologies impact genomics |
| title_full | How data science and AI-based technologies impact genomics |
| title_fullStr | How data science and AI-based technologies impact genomics |
| title_full_unstemmed | How data science and AI-based technologies impact genomics |
| title_short | How data science and AI-based technologies impact genomics |
| title_sort | how data science and ai based technologies impact genomics |
| topic | artificial intelligence deep learning genome-wide association study pharmacogenomics phenome-wide association study |
| url | https://journals.lww.com/10.4103/singaporemedj.SMJ-2021-438 |
| work_keys_str_mv | AT jinglin howdatascienceandaibasedtechnologiesimpactgenomics AT keeyuanngiam howdatascienceandaibasedtechnologiesimpactgenomics |