Machine learning for bio- and medical-informatics

许多领域都有海量的数据--从网络浏览器的日志文件，到设备的遥测数据，再到医院记录。这些数据中的模式可能非常有价值；例如，这些模式可以确定哪些机器需要预防性维护，用户可能想要访问哪些网站，S可能会购买哪些产品，甚至可以确定哪些患者对给定的治疗会有良好的反应。不幸的是，由于数据的数量和复杂性，这些模式可能很难提取。机器学习(ML)领域为这项任务提供了许多技术和工具。这项工作的大部分涉及理解、开发和使用工具，这些工具将实例(例如，一组患者及其各自的临床结果)的“标签数据集”作为输入，并返回一个分类器，该分类器可以将新的实例映射到标签--例如，确定具有给定微阵列的患者是否对某些治疗有良好的反应。

Many fields have massive amounts of data -- ranging from log files from web browsers, to telemetry data from equipment, to hospital records. The patterns within this data can be very valuable; eg these patterns may identify which machine needs preventative maintanence, which sites a user may want to visit and which products s/he is likely to purchase, or even identify which patients will respond well to a given treatment. Unfortunately, these patterns may be difficult to extract, due to both the quantity, and complexity, of the data. The field of machine learning (ML) provides many technologies, and tools, for this task. Much of this work involves understanding, developing and using tools that take as input a "labeled dataset" of instances (eg, a set of patients, coupled with their respective clinical outcomes), and returns a classifier, which can map novel instances to labels -- eg, to determine whether a patient, with a given microarray, will respond well to some treatment.