Using Machine Learning for Personalized Medicine

使用机器学习进行个性化医疗

• 批准号: RGPIN-2014-03854
• 负责人: Greiner, Russell
• 金额: $ 3.93万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635123
• 关键词: Using; Machine; Learning; Personalized; Medicine

The field of medicine is increasingly focusing on "personalized medicine": identifying the specific treatment that is best for Ms Smith, based on all of her attributes. Over the last decade, essentially all of my research has focused on this challenge. My lab and I have used the "machine learning" approach: finding relevant classifiers, based on datasets of historical patients. While standard machine learning systems were sufficient to learn classifiers for some of these tasks, many others require innovative extensions, (a) to find useful patterns in medical images (eg, extending conditional random fields to enable them to effectively locate brain tumors within a patient's MRI scan), or (b) to deal with the high dimensional data in today's "omics" datasets (eg, the 50,000 features in a gene expression microarray, or the 1 million features in a patient's Single Nucleotide Polymorphism [SNP] profile), or (c) to accommodate time sequence data, both the time spectra in fMRI data, and the sequential control components in helping a patient manage her Type I diabetes.We have also designed new foundational technologies -- eg, to efficiently collect the information required to produce effective classifiers ("budgeted learning"), and to develop a tool that learns a patient specific survival distribution (PSSP; think of a Kaplan-Meier curve, specific to Ms Smith herself).Over the next five years, I plan to continue working in this exciting area of enabling personalized medicine by developing the necessary tools. As before, I anticipate some tasks will involve applying standard techniques; these results will still be medically useful, and will help demonstrate machine learning to the medical community, as a way to produce truly personalized treatments. Others tasks will require further extensions to the innovations developed earlier -- such as additions to the PSSP system (eg, using L1-regularization, to identify which features are relevant at each time), and additional novel ways to apply reinforcement learning techniques to manage diabetes, and other chronic diseases. Many diseases, such as most cancers, are extremely heterogeneous -- not just wrt a population of subjects, but also within a single subject. We will explore ways to effectively model such diseases, as these models will be essential to identify the appropriate treatment for the individuals. Given the challenges (and costs) of collecting samples, many of the training datasets will actually be combinations of data from multiple sites. This leads to several problems: batch effects (due to technical differences between the different labs,etc.), and covariate shift (slightly different populations in each sub-study). I will continue to explore ways to detect, and then correct, these subtle, but extremely important, problems.I plan to devote most of my efforts, however, to finding effective ways for learning algorithms to use prior biological knowledge (eg, of metabolomic and/or signaling pathways) to improve the performance of learned classifiers. Given the imperfections inherent in today's pathway information, this will require (in parallel) identifying ways to improve that pathway information. My experience has shown that such information is essential to deal with today's (and even more-so, for tomorrow's) high-dimensional data (of microarrays, SNPs, CNVs, next generation sequencing, etc.), as there will, otherwise, be no way to identify the complex patterns associated with many multi-focal diseases, such as cancer. Hence, such analyses will be required to produce tools for personalized treatment for patients coping with these important diseases.

医学领域越来越关注“个性化医疗”：根据史密斯女士的所有属性，确定最适合她的特定治疗。在过去的十年里，基本上我所有的研究都集中在这一挑战上。我和我的实验室使用了“机器学习”方法：根据历史患者的数据集找到相关的分类器。虽然标准的机器学习系统足以学习其中一些任务的分类器，但许多其他任务需要创新的扩展，（a）在医学图像中找到有用的模式（例如，扩展条件随机场以使它们能够有效地在患者的MRI扫描中定位脑肿瘤），或（B）处理当今“组学”数据集中的高维数据（例如，基因表达微阵列中的50，000个特征，或患者单核苷酸多态性[SNP]谱中的100万个特征），或（c）适应时间序列数据，fMRI数据中的时间谱，以及帮助患者管理I型糖尿病的顺序控制组件。有效地收集产生有效分类器所需的信息（“预算学习”），并开发一种学习患者特定生存分布（PSSP;想想Kaplan-Meier曲线，特定于Smith女士本人）的工具。在接下来的五年里，我计划通过开发必要的工具，继续在实现个性化医疗这一令人兴奋的领域工作。和以前一样，我预计一些任务将涉及应用标准技术;这些结果仍然在医学上有用，并将有助于向医学界展示机器学习，作为一种产生真正个性化治疗的方法。其他任务将需要对早期开发的创新进行进一步扩展--例如添加到PSSP系统（例如，使用L1正则化来识别每次相关的特征），以及应用强化学习技术来管理的其他新方法糖尿病和其他慢性疾病。许多疾病，如大多数癌症，是非常异质性的-不仅是在一个受试者群体中，而且在单个受试者中。我们将探索有效模拟这些疾病的方法，因为这些模型对于确定个人的适当治疗至关重要。考虑到收集样本的挑战（和成本），许多训练数据集实际上是来自多个站点的数据的组合。这导致了几个问题：批量效应（由于不同实验室之间的技术差异等），和协变量变化（每个子研究中的人群略有不同）。我将继续探索检测并纠正这些微妙但极其重要的问题的方法，但我计划将大部分精力投入到寻找有效的学习算法方法，以使用先前的生物学知识（例如，代谢组学和/或信号通路）来提高学习分类器的性能。考虑到今天的路径信息中固有的不完善之处，这将需要（同时）确定改进路径信息的方法。我的经验表明，这些信息对于处理今天（甚至更重要，对于明天）的高维数据（微阵列，SNP，CNV，下一代测序等）至关重要，否则，将无法识别与许多多病灶疾病（例如癌症）相关的复杂模式。因此，需要进行这种分析，以便为应对这些重要疾病的患者提供个性化治疗工具。