Preconditioned random forest regression: Application to genome-wide study for radiotherapy toxicity prediction

Sangkyu Lee, Harry Ostrer, Sarah Kerns, Joseph O. Deasy, Barry Rosenstein, Jung Hun Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Urinary toxicity after radiotherapy (RT) limits the quality of life of prostate cancer patients, and clinically actionable prediction has yet to be achieved. We aim to exploit genome-wide variants to accurately identify patients at higher congenital toxicity risk. We applied preconditioned random forest regression (PRFR) to predict four urinary symptoms. For a weak stream endpoint, the PRFR model achieved an area under the curve (AUC) of 0.7 on holdout validation. Preconditioning enhanced the performance of random forest. Gene ontology (GO) analysis showed that neurogenic biological processes are associated with the toxicity. Upon further validation, the predictive model can be used to potentially benefit the health of prostate cancer patients treated with radiotherapy.

Original languageEnglish (US)
Title of host publicationACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Number of pages1
ISBN (Electronic)9781450347228
DOIs
Publication statusPublished - Aug 20 2017
Event8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017 - Boston, United States
Duration: Aug 20 2017Aug 23 2017

Other

Other8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017
CountryUnited States
CityBoston
Period8/20/178/23/17

    Fingerprint

Keywords

  • Genome wide association studies
  • Radiotherapy
  • Random forests

ASJC Scopus subject areas

  • Software
  • Biomedical Engineering
  • Health Informatics
  • Computer Science Applications

Cite this

Lee, S., Ostrer, H., Kerns, S., Deasy, J. O., Rosenstein, B., & Oh, J. H. (2017). Preconditioned random forest regression: Application to genome-wide study for radiotherapy toxicity prediction. In ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics Association for Computing Machinery, Inc. https://doi.org/10.1145/3107411.3108201