Supervised and unsupervised learning in biostatistics

由于计算和分子生物学的重大技术进步，现在可以获得巨大的生物医学数据库。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，数据几乎可以在所有观察层次上获得：社会、心理、临床、系统和分子。现在越来越需要进行综合，而且人们越来越认识到，必须将不同层次的观察联系起来，以增进了解。新的数据分析方法，似乎是适合的任务被认为是智能的，即能够模仿尽可能多的人类专家的思维。它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，例如真实的处理大量数据集。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并行性。我目前和未来的工作重点包括：1）基于统计模型的树生长算法，用于日益复杂的数据结构，例如来自多级系统的数据。2)基于统计模型的神经网络架构。3)模式发现（聚类）用于多级数据和其他复杂数据结构，无论是在探索模式还是基于模型的模式下。4)潜在的类模型，以解释复杂的动态模式观察变量通过简单的动态基本概念实体。

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.