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Sang Kyu Lee

Formats
  1. Journal Article (8)
  2. Meeting Abstract (8)
  3. Book Section (2)
  4. Conference Paper (1)
Groups
  1. Medical Physics (19)
  2. Sloan Kettering Institute Postdocs (19)
  3. Population Sciences Research Program (3)
  4. Radiotherapy Physics Service (3)
  5. Radiation Oncology, Department of (2)
  6. Center for Pancreatic Cancer Research (1)

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People
  1. Jung Hun Oh (13)
  2. Joseph Deasy (12)
  3. Ellen Yorke (2)
  4. Lei Zhang (2)
  5. Maria Thor (2)
  6. Alexander Kowalski (1)

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Years
  1. 2025 (1)
  2. 2024 (1)
  3. 2023 (1)
  4. 2022 (2)
  5. 2021 (3)
  6. 2020 (2)

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Publications
  1. Radiotherapy and Oncology (5)
  2. International Journal of Radiation Oncology, Biology, Physics (2)
  3. Journal of Applied Clinical Medical Physics (2)
  4. ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (1)
  5. Artificial Intelligence in Radiation Oncology (1)
  6. BJR Open (1)

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Publishers
  1. Elsevier Inc. (7)
  2. American College of Medical Physics (2)
  3. American Association of Physicists in Medicine (1)
  4. American Society of Clinical Oncology (1)
  5. Assoc Computing Machinery (1)
  6. British Institute of Radiology (1)

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Results (19)


Citation style sort is year ascending, and then by author or title. Author ascending is default.
  • Prediction of treatment (tx)-induced fatigue in breast cancer (BC) patients (pts) using machine learning on genome-wide association (GWAS) data in the prospective CANTO cohort
    Meeting Abstract

    Lee, S.; Deasy, J. O.; Oh, J. H.; Di Meglio, A.; Boyault, S.; Rousseau-Tsangaris, M.; Besse, C.; Thomas, E.; Boland-AugeĢ, A.; Cottu, P. H.; more...
    Journal of Clinical Oncology (2019)

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  • Determining the radiodensity range for data-driven quantification of radiation-induced lung fibrosis
    Meeting Abstract

    Wang, L. M.; Chatterjee, A.; Semionov, A.; Tsui, J.; Lee, S.; Yang, I.; Al Bulushi, Y.; Seuntjens, J.; Ybarra, N.
    Radiotherapy and Oncology (2019)

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  • Predicting genitourinary toxicity by machine learning on genome-wide single nucleotide polymorphisms
    Meeting Abstract

    Lee, S.; Oh, J.; Kerns, S.; Rosenstein, B.; Ostrer, H.; Deasy, J.
    Radiotherapy and Oncology (2018)

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  • Machine learning of radiogenomics SNP GWAS to predict complication risk and to identify key biological correlates
    Meeting Abstract

    Deasy, J.; Lee, S. K.; Oh, J. H.; Kerns, S.; Orstrer, H.; Rosenstein, B.
    Radiotherapy and Oncology (2018)

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  • Machine learning and radiogenomics: Lessons learned and future directions
    Journal Article

    Kang, J.; Rancati, T.; Lee, S.; Oh, J. H.; Kerns, S. L.; Scott, J. G.; Schwartz, R.; Kim, S.; Rosenstein, B. S.
    Frontiers in Oncology (2018)

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  • Machine learning on a genome-wide association study to predict late genitourinary toxicity after prostate radiation therapy
    Journal Article

    Lee, S.; Kerns, S.; Ostrer, H.; Rosenstein, B.; Deasy, J. O.; Oh, J. H.
    International Journal of Radiation Oncology, Biology, Physics (2018)

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  • Machine learning-based modeling to predict radiotherapy-induced genitourinary toxicity in prostate cancer using genome-wide association study
    Meeting Abstract

    Lee, S.; Oh, J.; Kerns, S.; Ostrer, H.; Rosenstein, B.; Deasy, J.
    Medical Physics (2017)

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

    Lee, S.; Ostrer, H.; Kerns, S.; Deasy, J. O.; Rosenstein, B.; Oh, J. H.
    ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2017)

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  • Transcriptional responses to ultraviolet and ionizing radiation: An approach based on graph curvature
    Conference Paper

    Chen, Y.; Oh, J. H.; Sandhu, R.; Lee, S.; Deasy, J. O.; Tannenbaum, A.
    Institute of Electrical and Electronics Engineers Inc. (2016)

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