Meta-analysis of macrophage nanoparticle targeting across blood and solid tumors using an eLDA Topic modeling Machine Learning approach

Brown, C.; Bilynsky, C. S. M.; Gainey, M.; Young, S.; Kitchin, J.; Wayne, E. C.

doi:10.1101/2023.06.29.547096

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bioRxiv · 10.1101/2023.06.29.547096

Meta-analysis of macrophage nanoparticle targeting across blood and solid tumors using an eLDA Topic modeling Machine Learning approach

Brown, C.·Bilynsky, C. S. M.·Gainey, M.·Young, S.·Kitchin, J.·Wayne, E. C.

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Abstract

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of the most common usages of nanoparticle targeting of macrophages in solid tumors. The data spans 20 years of literature, providing an extensive meta-analysis of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of meta-analysis provides a useful assessment tool for aggregating data about a large field.

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Brown, C., Bilynsky, C. S. M., Gainey, M., Young, S., Kitchin, J., Wayne, E. C.. 2023-06-30. Meta-analysis of macrophage nanoparticle targeting across blood and solid tumors using an eLDA Topic modeling Machine Learning approach. https://doi.org/10.1101/2023.06.29.547096

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