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 ch...

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Vydané v:	Briefings in bioinformatics Ročník 26; číslo 2
Hlavní autori:	Spooner, Annette, Moridani, Mohammad Karimi, Toplis, Barbra, Behary, Jason, Safarchi, Azadeh, Maher, Salim, Vafaee, Fatemeh, Zekry, Amany, Sowmya, Arcot
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 04.03.2025 Oxford Publishing Limited (England)
Predmet:	Algorithms Benchmarking Biological activity Breast Neoplasms - genetics Breast Neoplasms - metabolism Carcinoma, Hepatocellular - genetics Carcinoma, Hepatocellular - metabolism Data integration Ensemble learning Female Genomics - methods Hepatocellular carcinoma Humans Learning algorithms Liver Neoplasms - genetics Liver Neoplasms - metabolism Machine Learning Multiomics Predictions Sensory integration multi-class clinical outcome prediction late integration multi-omics cancer machine learning multi-modal hepatocellular carcinoma
ISSN:	1467-5463, 1477-4054, 1477-4054
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Popis
Shrnutí:	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 of expert’s model. These were compared to simple concatenation. We examine these methods using data from an in-house study on hepatocellular carcinoma, plus validation datasets on studies from breast cancer and irritable bowel disease. We develop models that achieve an area under the receiver operating curve of up to 0.85 and find that two boosted methods, PB-MVBoost and AdaBoost with soft vote were the best performing models. We also examine the stability of features selected and the size of the clinical signature. Our work shows that integrating complementary omics and data modalities with effective ensemble ML models enhances accuracy in multi-class clinical outcome predictions and produces more stable predictive features than individual modalities or simple concatenation. We provide recommendations for the integration of multi-modal multi-class data. Graphical Abstract Graphical Abstract
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1467-5463 1477-4054 1477-4054
DOI:	10.1093/bib/bbaf116