期刊
CELLS
卷 11, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/cells11050905
关键词
cancer; cell sorting; circulating tumor cells; microfluidics; machine-learning
类别
This article reviews the development history of microfluidics in cancer research and emphasizes the importance of machine learning in cancer studies, particularly in biomarker detection. Causality analysis plays a crucial role in optimizing microfluidic platforms.
Cancer metastasis is one of the primary reasons for cancer-related fatalities. Despite the achievements of cancer research with microfluidic platforms, understanding the interplay of multiple factors when it comes to cancer cells is still a great challenge. Crosstalk and causality of different factors in pathogenesis are two important areas in need of further research. With the assistance of machine learning, microfluidic platforms can reach a higher level of detection and classification of cancer metastasis. This article reviews the development history of microfluidics used for cancer research and summarizes how the utilization of machine learning benefits cancer studies, particularly in biomarker detection, wherein causality analysis is useful. To optimize microfluidic platforms, researchers are encouraged to use causality analysis when detecting biomarkers, analyzing tumor microenvironments, choosing materials, and designing structures.
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