Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogen...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-01-01
|
| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2023.1260276/full |
| _version_ | 1797361404025503744 |
|---|---|
| author | Alessia Mondello Michele Dal Bo Giuseppe Toffoli Maurizio Polano |
| author_facet | Alessia Mondello Michele Dal Bo Giuseppe Toffoli Maurizio Polano |
| author_sort | Alessia Mondello |
| collection | DOAJ |
| description | Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer. |
| first_indexed | 2024-03-08T15:53:13Z |
| format | Article |
| id | doaj.art-f954f1efaba5435baf7a725cd290b47c |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-03-08T15:53:13Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-f954f1efaba5435baf7a725cd290b47c2024-01-09T04:12:31ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-01-011410.3389/fphar.2023.12602761260276Machine learning in onco-pharmacogenomics: a path to precision medicine with many challengesAlessia MondelloMichele Dal BoGiuseppe ToffoliMaurizio PolanoOver the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.https://www.frontiersin.org/articles/10.3389/fphar.2023.1260276/fullpharmacogenomicsmachine learningomicstargeted therapydrug toxicitydrug efficacy |
| spellingShingle | Alessia Mondello Michele Dal Bo Giuseppe Toffoli Maurizio Polano Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges Frontiers in Pharmacology pharmacogenomics machine learning omics targeted therapy drug toxicity drug efficacy |
| title | Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges |
| title_full | Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges |
| title_fullStr | Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges |
| title_full_unstemmed | Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges |
| title_short | Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges |
| title_sort | machine learning in onco pharmacogenomics a path to precision medicine with many challenges |
| topic | pharmacogenomics machine learning omics targeted therapy drug toxicity drug efficacy |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2023.1260276/full |
| work_keys_str_mv | AT alessiamondello machinelearninginoncopharmacogenomicsapathtoprecisionmedicinewithmanychallenges AT micheledalbo machinelearninginoncopharmacogenomicsapathtoprecisionmedicinewithmanychallenges AT giuseppetoffoli machinelearninginoncopharmacogenomicsapathtoprecisionmedicinewithmanychallenges AT mauriziopolano machinelearninginoncopharmacogenomicsapathtoprecisionmedicinewithmanychallenges |