Skip to main content

Structure-leveraged methods in breast cancer risk prediction

Publication ,  Journal Article
Fan, J; Wu, Y; Yuan, M; Page, D; Liu, J; Ong, IM; Peissig, P; Burnside, E
Published in: Journal of Machine Learning Research
May 1, 2016

©2016 Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy Peissig and Elizabeth Burnside. Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case-control study, garnering 49 mammography descriptors and 77 high-frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future. Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these approaches. More specifically, we propose a new methodology by combining group penalty and lp (1 ≤ p ≤ 2) fusion penalty to improve breast cancer risk prediction, taking into account structure information in mammography descriptors and SNPs. We demonstrate that our method provides benefits that are both statistically significant and potentially significant to people's lives.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

May 1, 2016

Volume

17

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Fan, J., Wu, Y., Yuan, M., Page, D., Liu, J., Ong, I. M., … Burnside, E. (2016). Structure-leveraged methods in breast cancer risk prediction. Journal of Machine Learning Research, 17.
Fan, J., Y. Wu, M. Yuan, D. Page, J. Liu, I. M. Ong, P. Peissig, and E. Burnside. “Structure-leveraged methods in breast cancer risk prediction.” Journal of Machine Learning Research 17 (May 1, 2016).
Fan J, Wu Y, Yuan M, Page D, Liu J, Ong IM, et al. Structure-leveraged methods in breast cancer risk prediction. Journal of Machine Learning Research. 2016 May 1;17.
Fan, J., et al. “Structure-leveraged methods in breast cancer risk prediction.” Journal of Machine Learning Research, vol. 17, May 2016.
Fan J, Wu Y, Yuan M, Page D, Liu J, Ong IM, Peissig P, Burnside E. Structure-leveraged methods in breast cancer risk prediction. Journal of Machine Learning Research. 2016 May 1;17.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

May 1, 2016

Volume

17

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences