Work

Structure-Leveraged Methods in Breast Cancer Risk Prediction.

Year: 2016

Type: article

Abstract: ≤ 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.

Source: PubMed

Authors: Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M Ong, Peggy Peissig, Elizabeth Burnside

Institutions: University of Wisconsin–Madison, University of Washington, Marshfield Clinic Research Foundation, Marshfield Clinic Research Institute

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Environmental Health Sciences, U.S. National Library of Medicine

Awards: Mentoring And Research In Patient-Oriented Breast Cancer Diagnosis, The Center for Predictive Computational Phenotyping-1 Overall, Machine Learning for Identifying Adverse Drug Events, Institutional Clinical and Translational Science Award, Secure Sharing of Clinical History & Genetic Data: Empowering Predictive Pers. Me, Machine Learning for Improved Mammography Screening, Breast Cancer GWAS: Function and Environmental Interactions, UW Comprehensive Cancer Center Support

OpenAlex ID: W2467591991

FWCI: 1.918

Cites: 28

Cited by: 22

Related to: 10