A-Razzak M, Glen R C
Infolink Decision Services Ltd., London, U.K.
J Comput Aided Mol Des. 1992 Aug;6(4):349-83. doi: 10.1007/BF00125944.
Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.
最近，人工智能（AI）领域已经开发出了一些方法，特别是在使用规则归纳的专家系统领域，旨在从数据中提取规则。我们已将这些方法应用于分子序列分析，目的是生成具有预测性和可靠性的规则。规则归纳的输入由一些已知结果的示例（训练集）组成，输出是一系列树形结构的规则。与大多数其他分析方法不同，分析结果采用简单陈述的形式，易于解释。这些陈述很容易应用于新数据，既能给出分类又能给出正确的概率。规则归纳已应用于内部生成和已发表的定量构效关系（QSAR）数据集，并讨论了这些分析的方法、应用和结果。结果表明，在某些情况下，除了传统的统计和模式识别方法外，使用规则归纳作为一种补充技术将是有利的。
