Machine Learning: An Overview and Applications in Pharmacogenetics
This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/12/10/1511 |
| _version_ | 1797514496143523840 |
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| author | Giovanna Cilluffo Salvatore Fasola Giuliana Ferrante Velia Malizia Laura Montalbano Stefania La Grutta |
| author_facet | Giovanna Cilluffo Salvatore Fasola Giuliana Ferrante Velia Malizia Laura Montalbano Stefania La Grutta |
| author_sort | Giovanna Cilluffo |
| collection | DOAJ |
| description | This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics. |
| first_indexed | 2024-03-10T06:32:27Z |
| format | Article |
| id | doaj.art-1d4b6d91b7554baeac8e17a6ac25827c |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-10T06:32:27Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-1d4b6d91b7554baeac8e17a6ac25827c2023-11-22T18:21:04ZengMDPI AGGenes2073-44252021-09-011210151110.3390/genes12101511Machine Learning: An Overview and Applications in PharmacogeneticsGiovanna Cilluffo0Salvatore Fasola1Giuliana Ferrante2Velia Malizia3Laura Montalbano4Stefania La Grutta5Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, ItalyInstitute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, ItalyDepartment of Surgical Sciences, Dentistry, Gynecology and Pediatrics, Pediatric Division, University of Verona, 37134 Verona, ItalyInstitute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, ItalyInstitute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, ItalyInstitute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, ItalyThis narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.https://www.mdpi.com/2073-4425/12/10/1511pharmacogeneticssupervised machine learningunsupervised machine learning |
| spellingShingle | Giovanna Cilluffo Salvatore Fasola Giuliana Ferrante Velia Malizia Laura Montalbano Stefania La Grutta Machine Learning: An Overview and Applications in Pharmacogenetics Genes pharmacogenetics supervised machine learning unsupervised machine learning |
| title | Machine Learning: An Overview and Applications in Pharmacogenetics |
| title_full | Machine Learning: An Overview and Applications in Pharmacogenetics |
| title_fullStr | Machine Learning: An Overview and Applications in Pharmacogenetics |
| title_full_unstemmed | Machine Learning: An Overview and Applications in Pharmacogenetics |
| title_short | Machine Learning: An Overview and Applications in Pharmacogenetics |
| title_sort | machine learning an overview and applications in pharmacogenetics |
| topic | pharmacogenetics supervised machine learning unsupervised machine learning |
| url | https://www.mdpi.com/2073-4425/12/10/1511 |
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