LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis is a statistical method used for feature selection in machine learning and data analysis. In this context, it was used to identify a subset of genes from a larger set of cancer-related genes (CRGs) that were most strongly associated with the outcome being studied.
The use of LASSO regression analysis allowed researchers to identify 16 feature genes that had the strongest relationship with the outcome under investigation. These genes may be useful biomarkers for predicting disease progression or response to treatment, as well as providing insights into the underlying biology of the disease.
By reducing the number of genes being analyzed, LASSO regression can simplify complex datasets and improve accuracy in predictive modeling. This technique has been applied across various fields, including genetics, neuroscience, and economics, highlighting its versatility and usefulness in identifying key features within large datasets.
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