Genes, Vol. 12, Pages 722: Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning
Genes, Vol. 12, Pages 722: Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning
Genes doi: 10.3390/genes12050722
Authors:
Mahaly Baptiste
Sarah Shireen Moinuddeen
Courtney Lace Soliz
Hashimul Ehsan
Gen Kaneko
Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the...
Source: Genes - Category: Genetics & Stem Cells Authors: Mahaly Baptiste Sarah Shireen Moinuddeen Courtney Lace Soliz Hashimul Ehsan Gen Kaneko Tags: Review Source Type: research
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