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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Bibliographic Details
Published in:	Briefings in bioinformatics Vol. 26; no. 2
Main Authors:	Spooner, Annette, Moridani, Mohammad Karimi, Toplis, Barbra, Behary, Jason, Safarchi, Azadeh, Maher, Salim, Vafaee, Fatemeh, Zekry, Amany, Sowmya, Arcot
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 04.03.2025 Oxford Publishing Limited (England)
Subjects:	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
Online Access:	Get full text
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