Preconditioned random forest regression: Application to genome-wide study for radiotherapy toxicity prediction Meeting Abstract
| Authors: | Lee, S.; Ostrer, H.; Kerns, S.; Deasy, J. O.; Rosenstein, B.; Oh, J. H. |
| Abstract Title: | Preconditioned random forest regression: Application to genome-wide study for radiotherapy toxicity prediction |
| Meeting Title: | 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics |
| 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. © 2017 Copyright held by the owner/author(s). |
| Keywords: | genes; radiotherapy; patient monitoring; urology; forecasting; health; patient treatment; toxicity; bioinformatics; decision trees; prostate cancers; diseases; random forests; predictive modeling; biological process; genome-wide association studies; genome wide association studies; area under the curves; holdout validation; radiotherapy toxicities |
| Journal Title: | ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics |
| Meeting Dates: | 2017 Aug 20-23 |
| Meeting Location: | Boston, MA |
| ISSN: | 978-1-4503-4722-8 |
| Publisher: | Assoc Computing Machinery |
| Date Published: | 2017-01-01 |
| Start Page: | 593 |
| Language: | English |
| DOI: | 10.1145/3107411.3108201 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference Paper -- Export Date: 2 November 2017 -- Source: Scopus |
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