Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification.

ABSTRACT: Advances in high-throughput technologies encourage the generation of large amounts of multiomics data to investigate complex diseases, including breast cancer. Given that the aetiologies of such diseases extend beyond a single biological entity, and that essential biological information can be carried by all data regardless of data type, integrative analyses are needed to identify clinically relevant patterns. To facilitate such analyses, we present a permutation-based framework for random forest methods which simultaneously allows the unbiased integration of mixed-type data and assessment of relative feature importance. Through simulation studies and machine learning datasets, the performance of the approach was evaluated. The results showed minimal multicollinearity and limited overfitti

SUBMITTER: Quist J 

PROVIDER: S-EPMC7956671 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature