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Machine learning reveals cellular morphological subtypes and molecular interpretations of LGG

 Last Update: 2022-09-09
 Source: Internet
 Author: User

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Recently, Xiao-Ping Liu of the Department of Biological Systems and Engineering of lawrence Berkeley National Laboratory in the United States has applied machine learning to integrate and verify clinical data and database data, conduct clinical biological evaluation, construct cell morphological background characterization, and apply immunohistochemistry to develop and validate machine learning frameworks for cell morphology measurement subtypes, which are used to discover cell morphological subtypes

——Excerpt from the article chapter

【Ref: Liu XP, et al.

Research background

Low-grade gliomas (LGG) are highly heterogeneous at both the histopathological and molecular levels, with significant differences in clinical outcomes

The authors used artificial intelligence techniques to identify cell morphology measurement biomarkers (CMBs)

Research results

The results showed that the researchers used histopathological slice images to extract information from cell morphological measurement biomarkers, and completed identification and external validation

In summary, the research team developed and validated machine learning pipeline-cell morphology measurement subtypes for the discovery of cellular morphological subtypes

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