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AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care

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dc.contributor.author Zodwa, Dlamini, Z
dc.contributor.author Skepu, Amanda
dc.contributor.author Kim, N
dc.contributor.author Mkhabele, M
dc.contributor.author Khanyile, R
dc.contributor.author Molefi, T
dc.contributor.author Mbatha, S
dc.contributor.author Setlai, B
dc.contributor.author Mulaudzi, T
dc.contributor.author Mabongo, M
dc.date.accessioned 2022-12-02T09:33:14Z
dc.date.available 2022-12-02T09:33:14Z
dc.date.issued 2022-05
dc.identifier.citation Zodwa, D., Skepu, A., Kim, N., Mkhabele, M., Khanyile, R., Molefi, T., Mbatha, S. & Setlai, B. et al. 2022. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. <i>Informatics in Medicine Unlocked, 31.</i> http://hdl.handle.net/10204/12552 en_ZA
dc.identifier.issn 2352-9148
dc.identifier.uri https://doi.org/10.1016/j.imu.2022.100965
dc.identifier.uri http://hdl.handle.net/10204/12552
dc.description.abstract Precision medicine is the personalization of medicine to suit a specific group of people or even an individual patient, based on genetic or molecular profiling. This can be done using genomic, transcriptomic, epigenomic or proteomic information. Personalized medicine holds great promise, especially in cancer therapy and control, where precision oncology would allow medical practitioners to use this information to optimize the treatment of a patient. Personalized oncology for groups of individuals would also allow for the use of population group specific diagnostic or prognostic biomarkers. Additionally, this information can be used to track the progress of the disease or monitor the response of the patient to treatment. This can be used to establish the molecular basis for drug resistance and allow the targeting of the genes or pathways responsible for drug resistance. Personalized medicine requires the use of large data sets, which must be processed and analysed in order to identify the particular molecular patterns that can inform the decisions required for personalized care. However, the analysis of these large data sets is difficult and time consuming. This is further compounded by the increasing size of these datasets due to technologies such as next generation sequencing (NGS). These difficulties can be met through the use of artificial intelligence (AI) and machine learning (ML). These computational tools use specific neural networks, learning methods, decision making tools and algorithms to construct and improve on models for the analysis of different types of large data sets. These tools can also be used to answer specific questions. Artificial intelligence can also be used to predict the effects of genetic changes on protein structure and therefore function. This review will discuss the current state of the application of AI to omics data, specifically genomic data, and how this is applied to the development of personalized or precision medicine on the treatment of cancer. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S2352914822001113 en_US
dc.source Informatics in Medicine Unlocked, 31 en_US
dc.subject Artificial intelligence en_US
dc.subject AI en_US
dc.subject Cancer genomics en_US
dc.subject Targeted cancer therapies en_US
dc.title AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care en_US
dc.type Article en_US
dc.description.pages 12pp en_US
dc.description.note © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). en_US
dc.description.cluster Next Generation Health en_US
dc.description.impactarea Human Molecular Diagnostics en_US
dc.identifier.apacitation Zodwa, D., Skepu, A., Kim, N., Mkhabele, M., Khanyile, R., Molefi, T., ... Mabongo, M. (2022). AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. <i>Informatics in Medicine Unlocked, 31</i>, http://hdl.handle.net/10204/12552 en_ZA
dc.identifier.chicagocitation Zodwa, Dlamini, Amanda Skepu, N Kim, M Mkhabele, R Khanyile, T Molefi, S Mbatha, B Setlai, T Mulaudzi, and M Mabongo "AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care." <i>Informatics in Medicine Unlocked, 31</i> (2022) http://hdl.handle.net/10204/12552 en_ZA
dc.identifier.vancouvercitation Zodwa D, Skepu A, Kim N, Mkhabele M, Khanyile R, Molefi T, et al. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. Informatics in Medicine Unlocked, 31. 2022; http://hdl.handle.net/10204/12552. en_ZA
dc.identifier.ris TY - Article AU - Zodwa, Dlamini, Z AU - Skepu, Amanda AU - Kim, N AU - Mkhabele, M AU - Khanyile, R AU - Molefi, T AU - Mbatha, S AU - Setlai, B AU - Mulaudzi, T AU - Mabongo, M AB - Precision medicine is the personalization of medicine to suit a specific group of people or even an individual patient, based on genetic or molecular profiling. This can be done using genomic, transcriptomic, epigenomic or proteomic information. Personalized medicine holds great promise, especially in cancer therapy and control, where precision oncology would allow medical practitioners to use this information to optimize the treatment of a patient. Personalized oncology for groups of individuals would also allow for the use of population group specific diagnostic or prognostic biomarkers. Additionally, this information can be used to track the progress of the disease or monitor the response of the patient to treatment. This can be used to establish the molecular basis for drug resistance and allow the targeting of the genes or pathways responsible for drug resistance. Personalized medicine requires the use of large data sets, which must be processed and analysed in order to identify the particular molecular patterns that can inform the decisions required for personalized care. However, the analysis of these large data sets is difficult and time consuming. This is further compounded by the increasing size of these datasets due to technologies such as next generation sequencing (NGS). These difficulties can be met through the use of artificial intelligence (AI) and machine learning (ML). These computational tools use specific neural networks, learning methods, decision making tools and algorithms to construct and improve on models for the analysis of different types of large data sets. These tools can also be used to answer specific questions. Artificial intelligence can also be used to predict the effects of genetic changes on protein structure and therefore function. This review will discuss the current state of the application of AI to omics data, specifically genomic data, and how this is applied to the development of personalized or precision medicine on the treatment of cancer. DA - 2022-05 DB - ResearchSpace DP - CSIR J1 - Informatics in Medicine Unlocked, 31 KW - Artificial intelligence KW - AI KW - Cancer genomics KW - Targeted cancer therapies LK - https://researchspace.csir.co.za PY - 2022 SM - 2352-9148 T1 - AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care TI - AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care UR - http://hdl.handle.net/10204/12552 ER - en_ZA
dc.identifier.worklist 26034 en_US


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