Using Biological Constraints to Improve Prediction in Precision Oncology

Abstract excerpt

<h4>Summary</h4> Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance in different datasets. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying differ...

Topics

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• Bioinformatics and Genomic Networks
• Bladder and Urothelial Cancer Treatments
• Cancer Genomics and Diagnostics
• Epigenetics and DNA Methylation
• Radiomics and Machine Learning in Medical Imaging
• Urinary and Genital Oncology Studies

Identifiers and source

Literature Corpus work

c94202a8-b8fe-57db-a2ad-de597f5c4aae

DOI

10.1101/2021.05.25.445604