Supervised and unsupervised learning in biostatistics

生物统计学中的监督和非监督学习

• 批准号: 9120-2007
• 负责人: Ciampi, Antonio
• 金额: $ 1.17万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=361383
• 关键词: Supervised; unsupervised; learning; biostatistics

As consequence of major technical advances in computing and in molecular biology, huge databases of biomedical interest are now available. In order to take advantage of the potential information they contain, new methods of data analysis are needed. One challenging aspect of the effort required, is that data are available at virtually all levels of observation: social, psychological, clinical, systemic and molecular. There is a growing need of synthesis and it is increasingly recognized that it is essential to link distinct levels of observation to improve understanding. The new methods of data analysis that seem to be appropriate for the task are considered intelligent, i.e. able to mimic as much as possible the thinking of human experts. They borrow substantially from the discipline known as machine learning, but with specific attention to problems of statistical inference and to practical needs, such as dealing in real time with huge data sets. Indeed, relatively new terms have been created in the last 15-20 years, such as data mining and statistical learning theory.In my work I attempt to fully exploit the parallelism between the cognitive strategies of an expert and some computer-based tasks of data analysis. Highlights of my current and future work include: 1) Tree-growing algorithms based on statistical models for increasingly complex data structures, e.g. data from multi-level systems. 2) Neural Network architectures based on statistical models. 3) Pattern discovery (clustering) for multilevel data and other complex data structures, both in an exploratory and in a model based mode. 4) Latent class models to explain complex dynamical patterns in observed variables via simpler dynamics of underlying conceptual entities.

由于计算和分子生物学方面的重大技术进步，现在已经有了生物医学感兴趣的大型数据库。为了利用它们所包含的潜在信息，需要新的数据分析方法。所需努力的一个具有挑战性的方面是，几乎所有级别的观察都可以获得数据：社会、心理、临床、系统和分子。对综合的需求越来越大，人们越来越认识到，必须将不同层次的观察联系起来，以增进理解。似乎适合这项任务的新的数据分析方法被认为是智能的，即能够尽可能地模仿人类专家的思维。他们大量借鉴了被称为机器学习的学科，但特别关注统计推断问题和实际需求，例如实时处理海量数据集。事实上，在过去的15-20年里，相对较新的术语已经被创造出来，比如数据挖掘和统计学习理论。在我的工作中，我试图充分利用专家的认知策略和一些基于计算机的数据分析任务之间的平行关系。我目前和未来工作的亮点包括：1)基于统计模型的树生长算法，用于日益复杂的数据结构，例如来自多层系统的数据。2)基于统计模型的神经网络结构。3)以探索性和基于模型的模式对多级数据和其他复杂数据结构进行模式发现(聚类)。4)潜在类模型，通过基本概念实体的更简单的动力学来解释观察变量中的复杂动态模式。