Regularized Generalized Linear Models to Disclose Host-Microbiome Associations in Colorectal Cancer

What is it about?

This study aims to optimize the ridge, elastic net, and lasso regularized generalized linear models (GLM), widely used for supervised machine learning, for multiclass classification tasks (healthy/adenoma/carcinoma). The models are applied to a benchmark gut microbiome dataset using raw and transformed data.

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Why is it important?

These findings are expected to contribute to the definition of CRC-microbiome signatures to be further validated in microbiome-related therapy studies.