用布尔方法解读癌症生物学

2016 IEEE International High Level Design Validation and Test Workshop (HLDVT) Pub Date : 2016-10-01 DOI:10.1109/HLDVT.2016.7748269

Subarna Sinha, D. Dill

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引用次数: 0

摘要

布尔暗示(if-then规则)提供了一种概念简单、统一且高度可扩展的方法来查找随机变量对之间的关联。在本文中，我们描述了如何从大型异构癌症数据集中推导布尔含义。我们展示了布尔蕴涵的两个应用，以发现癌症生物学中新的可操作的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deciphering cancer biology using boolean methods

Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we describe how Boolean implications can be derived from large, heterogeneous cancer data sets. We demonstrate two applications of Boolean implications to discover new actionable insights in cancer biology.

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2016 IEEE International High Level Design Validation and Test Workshop (HLDVT)

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