Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning
This work addresses the problem of improving diagnostic accuracy for HCC and other diseases like breast cancer and irritable bowel disease using multi-modal data, but it is incremental as it compares existing ensemble methods rather than introducing a fundamentally new approach.
The study tackled the challenge of integrating multi-omics data for early diagnosis of hepatocellular carcinoma (HCC) by comparing ensemble machine learning methods, achieving an area under the curve (AUC) of up to 0.85, with PB-MVBoost and Adaboost with soft vote identified as the best-performing models.
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, including high dimensionality and varying size, statistical distribution, scale and signal strength between modalities. In this work we compare the performance of a variety of ensemble machine learning 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, iii) a multi-modal Adaboost model using a hard vote, a soft vote and a meta learner to integrate the modalities on each boosting round, the PB-MVBoost model and a novel application of a mixture of experts model. These were compared to simple concatenation as a baseline. We examine these methods using data from an in-house study on hepatocellular carcinoma (HCC), along with four validation datasets on studies from breast cancer and irritable bowel disease (IBD). Using the area under the receiver operating curve as a measure of performance we develop models that achieve a performance value of up to 0.85 and find that two boosted methods, PB-MVBoost and Adaboost with a soft vote were the overall best performing models. We also examine the stability of features selected, and the size of the clinical signature determined. Finally, we provide recommendations for the integration of multi-modal multi-class data.