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| http://dx.doi.org/10.3389/fpain.2023.1290621 | DOI Listing |
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Cancer biomarkers can be derived from tumor cells or neighboring cells within the tumor microenvironment. Over the past few decades, various molecular markers, including DNA (mutations, copy number variations), RNA (mRNA, microRNA, circular RNA), proteins, and metabolites, have been identified with the aid of rapidly evolving technologies. Some of these markers have demonstrated potential clinical utility, while others have provided new insights into the deregulation of normal molecular and cellular processes that lead to tumorigenesis.
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Machine learning (ML) algorithms hold significant potential for extracting valuable clinical information from big data, surpassing the processing capabilities of the human brain. However, it would be naïve to believe that ML algorithms can consistently transform data into actionable insights. Clinical studies suggest that in some instances, they tell clinicians what they already know or can plainly see.
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