About the project
To address the pressing clinical demands of today, it is crucial to implement models that select minimal, cost-effective features. Feature selection in machine learning aims to fulfill this need by identifying the most predictive biomarkers with minimal redundancy. We have developed a multi-omics ensemble feature selection (EFS) approach that identifies the most significant biomarkers for a given cohort of patients. Our approach leverages multiple machine learning algorithms to discover optimal features for classification, regression, and survival analysis tasks.
The EFS method ranks features using voting theory, ensuring that all ensemble model perspectives are considered. The optimal number of selected features is determined through a Pareto-based knee-point identification method, providing a trade-off between sparsity and performance. When applied to multi-omics datasets from pancreatic cancer studies, our approach successfully identifies minimal biomarkers relevant to both the clinical outcome and the underlying biology of the disease. Overall, EFS offers a reliable and clinically valuable tool for biomarker discovery in cancer research.
Project team
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Postdoc Researcher John Zobolas, Oslo University Hospital, Department of Cancer Genetics
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Postdoctoral Fellow Anne-Marie George, University of Oslo, Department of Informatics
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Professor Tero Aittokallio, University of Oslo, Institute of Basic Medical Sciences