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Article 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.
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| http://dx.doi.org/10.1002/brb3.70843 | DOI Listing |
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417957 | PMC |
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