Supervised and unsupervised learning in biostatistics

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，出现了一些相对较新的术语，比如数据挖掘和统计学习理论。

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.