Ze Li, Kai Zhao, Hongyu Liu, Jialin Liu, Xu Chen, Wentao Hu, Er Wen, Kai Zhang, Ling Chen
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引用次数: 0
Abstract
Background
The gut microbiota plays a crucial role in the development of glioma. With the evolution of artificial intelligence technology, applying AI to analyze the vast amount of data from the gut microbiome indicates the potential that artificial intelligence and computational biology hold in transforming medical diagnostics and personalized medicine.
Methods
We conducted metagenomic sequencing on stool samples from 42 patients diagnosed with glioma after operation and 30 non-intracranial tumor patients and developed a Gradient Boosting Machine (GBM) machine learning model to predict the glioma patients based on the gut microbiome data.
Results
The AUC-ROC for the GBM model was 0.79, indicating a good level of discriminative ability.
Conclusions
This method's efficacy in discriminating between glioma cells and normal controls underscores the potential of machine learning models in leveraging large datasets for clinical insights.
期刊介绍:
Brain and Behavior is supported by other journals published by Wiley, including a number of society-owned journals. The journals listed below support Brain and Behavior and participate in the Manuscript Transfer Program by referring articles of suitable quality and offering authors the option to have their paper, with any peer review reports, automatically transferred to Brain and Behavior.
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