Biomaterial Database

What are artificial neural networks and what they can do? 2005

Vlastimil Dohnal, and Kamil Kuca, and Daniel Jun

Department of Food Technology, Mendel University of Agriculture and Forestry, Brno, Czech Republic. dohnal@mendelu.cz

The artificial neural networks (ANN) are very often applied in many areas of toxicology for the solving of complex problems, such as the prediction of chemical compound properties and quantitative structure-activity relationship. The aim of this contribution is to give the basic knowledge about conception of ANN, theirs division and finally, the typical application of ANN will be discussed. Due to the diversity of architectures and adaptation algorithms, the ANNs are used in the broad spectrum of applications from the environmental processes modeling, through the optimization to quantitative structure-activity relationship (QSAR) methods. In addition, especially ANNs with Kohonen learning are very effective classification tool. The ANNs are mostly applied in cases, where the commonly used methods does not work.

UI	MeSH Term	Description	Entries
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D014116	Toxicology	The science concerned with the detection, chemical composition, and biological action of toxic substances or poisons and the treatment and prevention of toxic manifestations.	Toxinology,Evidence Based Toxicology,Evidence-Based Toxicology,Based Toxicologies, Evidence,Based Toxicology, Evidence,Evidence Based Toxicologies,Evidence-Based Toxicologies,Toxicologies, Evidence Based,Toxicologies, Evidence-Based,Toxicology, Evidence Based,Toxicology, Evidence-Based
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D021281	Quantitative Structure-Activity Relationship	A quantitative prediction of the biological, ecotoxicological or pharmaceutical activity of a molecule. It is based upon structure and activity information gathered from a series of similar compounds.	Structure Activity Relationship, Quantitative,3D-QSAR,QSAR,QSPR Modeling,Quantitative Structure Property Relationship,3D QSAR,3D-QSARs,Modeling, QSPR,Quantitative Structure Activity Relationship,Quantitative Structure-Activity Relationships,Relationship, Quantitative Structure-Activity,Relationships, Quantitative Structure-Activity,Structure-Activity Relationship, Quantitative,Structure-Activity Relationships, Quantitative