Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction.

ABSTRACT: The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the analysis of multi-modal, multi-omics data presents many challenges. In this work, we compare the performance of a variety of ensemble machine learning (ML) algorithms that are capable of late integration of multi-class data from different modalities. The ensemble methods and their variations tested were (i) a voting ensemble, with hard and soft vote, (ii) a meta learner, and (iii) a multi-modal AdaBoost model using hard vote, soft vote, and meta learner to integrate the modalities on each boosting round, the PB-MVBoost model and a novel application of a mixture

SUBMITTER: Spooner A 

PROVIDER: S-EPMC11926982 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature