Rawlings C J, Fox J P
Biomedical Informatics Unit, Imperial Cancer Research Fund, London, U.K.
Philos Trans R Soc Lond B Biol Sci. 1994 Jun 29;344(1310):353-62; discussion 362-3. doi: 10.1098/rstb.1994.0074.
Over the past ten years, molecular biologists and computer scientists have experimented with various computational methods developed in artificial intelligence (AI). AI research has yielded a number of novel technologies, which are typified by an emphasis on symbolic (non-numerical) programming methods aimed at problems which are not amenable to classical algorithmic solutions. Prominent examples include knowledge-based and expert systems, qualitative simulation and artificial neural networks and other automated learning techniques. These methods have been applied to problems in data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the recognition of active genes in genomic sequences, the assembly of physical and genetic maps and protein structure prediction. This paper outlines the principal methods, surveys the findings to date, and identifies the promising trends and current limitations.
在过去十年中，分子生物学家和计算机科学家对人工智能（AI）领域开发的各种计算方法进行了试验。人工智能研究产生了许多新技术，其特点是强调针对那些不适合传统算法解决方案的问题的符号（非数值）编程方法。突出的例子包括基于知识的专家系统、定性模拟、人工神经网络以及其他自动学习技术。这些方法已应用于数据分析、高级数据库构建和生物系统建模等问题。目前正在取得实际成果，特别是在基因组序列中活性基因的识别、物理图谱和遗传图谱的组装以及蛋白质结构预测方面。本文概述了主要方法，综述了迄今为止的研究结果，并指出了有前景的趋势和当前的局限性。
