12 Result(s)
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Protocol
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Artificial Intelligence
Computer Science, general
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Neural Networks Predict Protein Structure and Function
Both supervised and unsupervised neural networks have been applied to the prediction of protein structure and function. Here, we focus on feedforward neural networks and describe how these learning machines ca...
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Artificial Neural Networks in Biology and Chemistry—The Evolution of a New Analytical Tool
Once regarded as an eccentric and unpromising algorithm for the analysis of scientific data, the neural network has been developed in the last decade into a powerful computational tool. Its use now spans all a...
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Bayesian Regularization of Neural Networks
Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a math...
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Artificial Neural Network Modeling in Environmental Toxicology
Artificial neural networks are increasingly used in environmental toxicology to find complex relationships between the ecotoxicity of xenobiotics and their structure or physicochemical properties. The raison d...
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Application of Artificial Neural Networks for Decision Support in Medicine
The emergence of drug resistant pathogens can reduce the efficacy of drugs commonly used to treat infectious diseases. Human immunodeficiency virus (HIV) is particularly sensitive to drug selection pressure, r...
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Peptide Bioinformatics- Peptide Classification Using Peptide Machines
Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, such as functional site prediction, protein structure identification, and protein funct...
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Associative Neural Network
An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memor...
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The Extraction of Information and Knowledge from Trained Neural Networks
In the past, neural networks were viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed that extract ...
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Overview of Artificial Neural Networks
The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modern drug discovery research requires so...
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Kohonen and Counterpropagation Neural Networks Applied for Map** and Interpretation of IR Spectra
The principles of learning strategy of Kohonen and counterpropagation neural networks are introduced. The advantages of unsupervised learning are discussed. The self-organizing maps produced in both methods ar...
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Neural Networks in Analytical Chemistry
This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-laye...
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Neural Networks in Building QSAR Models
This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predomin...
