Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.

ABSTRACT: Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n). Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly

SUBMITTER: Tian L 

PROVIDER: S-EPMC10377046 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature