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Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning by Spooner, Annette; Moridani, Mohammad Karimi; Safarchi, Azadeh; Maher, Salim; Vafaee, Fatemeh; Zekry, Amany; Sowmya, Arcot is a scholarly article available to read on EtoBox.

What is Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning about?

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

Author

Spooner, Annette; Moridani, Mohammad Karimi; Safarchi, Azadeh; Maher, Salim; Vafaee, Fatemeh; Zekry, Amany; Sowmya, Arcot

Published

2024

Language

EN