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.
