Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

被引:2
作者
Kosvyra Α. [1 ]
Karadimitris Α. [2 ]
Papaioannou Μ. [3 ]
Chouvarda I. [1 ]
机构
[1] Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki
[2] Centre for Haematology and Hugh and Josseline Langmuir Centre for Myeloma Research, Department of Immunology and Inflammation, Imperial College London, Department of Haematology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, Du Cane Road, Lo
[3] Hematology Unit, 1st Dept of Internal Medicine, AHEPA Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki
关键词
Integrated network analysis; Machine learning; Multi-omics;
D O I
10.1016/j.compbiomed.2024.108735
中图分类号
学科分类号
摘要
Background: Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction. Method: This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods. Results: Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques. Conclusions: This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future. © 2024 Elsevier Ltd
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