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Platelet RNA Sequencing Data Through the Lens of Machine Learning

Abstract

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

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DOI:
Digital Object Identifier (open in new tab) 10.3390/cancers15082336
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Creative Commons: CC-BY open in new tab

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
Cancers no. 15,
ISSN: 2072-6694
Language:
English
Publication year:
2023
Bibliographic description:
Cygert S., Pastuszak K., Górski F., Sieczczyński M., Juszczyk P., Rutkowski A., Lewalski S., Różański R., Jopek M. A., Jassem J., Czyżewski A., Wurdinger T., Best M. G., Żaczek A., Supernat A.: Platelet RNA Sequencing Data Through the Lens of Machine Learning// Cancers -Vol. 15,iss. 8 (2023), s.2336-
DOI:
Digital Object Identifier (open in new tab) 10.3390/cancers15082336
Sources of funding:
  • This work has been partially supported by statutory funds of the Electronics, Telecommunications and Informatics Faculty, Gdansk University of Technology.
Verified by:
Gdańsk University of Technology

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