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通过人工神经网络对复杂数据进行预测性非线性建模。

Predictive non-linear modeling of complex data by artificial neural networks.

作者信息

Almeida Jonas S

机构信息

Department of Biometry and Epidemiology, Medical University South Carolina, 135 Rutledge Avenue, PO Box 250551, Charleston SC 29425, USA.

出版信息

Curr Opin Biotechnol. 2002 Feb;13(1):72-6. doi: 10.1016/s0958-1669(02)00288-4.

Abstract

An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences.

摘要

人工神经网络(ANN)是一种人工智能工具,可直接从实验数据中识别任意非线性多参数判别函数。对于因变量和自变量之间的依赖关系未知或非常复杂的应用,人工神经网络的使用越来越普遍。这种机器学习技术大致可描述为一种通用代数函数,它将直接从实验数据中区分信号和噪声。人工神经网络在复杂关系中的应用使其在生物系统研究中极具吸引力。最近的应用包括对表达谱以及基因组和蛋白质组序列的分析。

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