Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-07-04

AUTHORS

Kevin Y. X. Wang, Gulietta M. Pupo, Varsha Tembe, Ellis Patrick, Dario Strbenac, Sarah-Jane Schramm, John F. Thompson, Richard A. Scolyer, Samuel Muller, Garth Tarr, Graham J. Mann, Jean Y. H. Yang

ABSTRACT

In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies. More... »

PAGES

85

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41746-022-00618-5

DOI

http://dx.doi.org/10.1038/s41746-022-00618-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1149201740

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35788693


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