Structure-Leveraged Methods in Breast Cancer Risk Prediction.

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2016-12-01
Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M Ong, Peggy Peissig, Elizabeth Burnside
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引用次数: 0

Abstract

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 [Formula: see text] (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.

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乳腺癌风险预测中的结构杠杆方法。
在追求精准医学的过程中,预测乳腺癌风险一直是医学研究的一个目标。本研究的目的是开发新的惩罚方法,利用电子健康记录中的结构信息来提高乳腺癌风险预测。我们进行了一项回顾性病例对照研究,从现有的个性化医学数据库中收集了49个乳房x线摄影描述符和77个高频/低外显率单核苷酸多态性(snp)。结构化乳房x光检查报告和乳房成像特征长期以来一直是标准电子健康记录(EHR)的一部分,遗传标记可能在不久的将来也会成为标准电子健康记录的一部分。Lasso及其变体是广泛使用的集成学习和特征选择方法,我们的方法贡献是将特征之间的依赖结构纳入这些方法中。更具体地说,我们提出了一种新的方法,结合群体惩罚和[公式:见文本](1≤p≤2)融合惩罚来提高乳腺癌风险预测,同时考虑到乳房x光描述符和snp的结构信息。我们证明,我们的方法对人们的生活既有统计学意义,也有潜在意义。
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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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