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Abstract
Liquid biopsy is a useful, minimally invasive diagnostic and monitoring tool for cancer disease. Yet, developing accurate methods, given the potentially large number of input features, and usually small datasets size remains very challenging. Recently, a novel feature parameterization based on the RNA-sequenced platelet data which uses the biological knowledge from the Kyoto Encyclopedia of Genes and Genomes, combined with a classifier based on the Convolutional Neural Network (CNN), allowed significantly improving the classification accuracy. In this work, we take a closer look at this approach and find that similar results can be obtained using significantly smaller models. Additionally, competitive results were achieved using gradient boosting. Since it has another advantage of adding interpretability to the model, we further analyze it in this work.
Citations
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Authors (7)
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Anna Supernat dr
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Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Cygert S., Górski F., Juszczyk P., Lewalski S., Pastuszak K., Czyżewski A., Supernat A.: Towards Cancer Patients Classification Using Liquid Biopsy// Predictive Intelligence in Medicine/ : , 2021, s.221-230
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-030-87602-9_21
- Sources of funding:
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- Medical University of Gdańsk statutory work (ST-23, 02-0023/07)
- SONATA grant of the National Science Centre (2018/31/D/NZ5/01263)
- Project -
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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