The SNNS Neural Network Simulator

Zell, Andreas; Mache, Niels; Sommer, Tilman; Korb, Thomas

doi:10.1007/978-3-662-08896-8_60

Cited by 13 publications

(9 citation statements)

References 4 publications

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“…Neural Network Analysis-As an alternative and quantitative approach to visual analysis, a feed-forward neural network was constructed and tested using the Stuttgart neural network system (SNNS) (20). The SNNS package was chosen for creating and executing the neural network because of its ease of use, availability, and flexibility.…”

Section: Methodsmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Type III Antifreeze Protein Structure and Function

Graether

Deluca

Baardsnes

et al. 1999

Journal of Biological Chemistry

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Some cold water marine fishes avoid cellular damage because of freezing by expressing antifreeze proteins (AFPs) that bind to ice and inhibit its growth; one such protein is the globular type III AFP from eel pout. Despite several studies, the mechanism of ice binding remains unclear because of the difficulty in modeling the AFP-ice interaction. To further explore the mechanism, we have determined the x-ray crystallographic structure of 10 type III AFP mutants and combined that information with 7 previously determined structures to mainly analyze specific AFP-ice interactions such as hydrogen bonds. Quantitative assessment of binding was performed using a neural network with properties of the structure as input and predicted antifreeze activity as output. Using the cross-validation method, a correlation coefficient of 0.60 was obtained between measured and predicted activity, indicating successful learning and good predictive power. A large loss in the predictive power of the neural network occurred after properties related to the hydrophobic surface were left out, suggesting that van der Waal's interactions make a significant contribution to ice binding. By combining the analysis of the neural network with antifreeze activity and x-ray crystallographic structures of the mutants, we extend the existing ice-binding model to a two-step process: 1) probing of the surface for the correct ice-binding plane by hydrogen-bonding side chains and 2) attractive van der Waal's interactions between the other residues of the ice-binding surface and the ice, which increases the strength of the protein-ice interaction. Many poikilothermic organisms have developed antifreeze proteins (AFPs)1 to resist freezing. Five classes of structurally diverse antifreeze proteins have been found in fish (for a review, see Ref. 1). These proteins act by adsorbing to the surface of ice and increasing the curvature of ice fronts between the bound AFPs (2). The freezing point at the surface is depressed, which in turn inhibits the growth of ice crystals. The difference between the temperature at which the ice begins to grow (burst point) and the temperature at which the ice crystal melts is known as thermal hysteresis (TH) and is used as a measure of AFP activity.Recently, several structures of type III AFP have been determined (4 -6). Based on the high-resolution x-ray structure (4), a model was proposed whereby surface adsorption occurs through a hydrogen bond match between the side chains of Gln-9, Asn-14, Thr-15, Thr-18, Gln-44, and the ice prism plane {1010}. These polar residues form part of a flat, amphipathic face that is thought to be the ice-binding surface (Fig. 1). However, the significance of the contribution from hydrogen bonds to the AFP-ice interaction has been questioned by several studies since then. A supposedly conservative change of Thr to Ser in type I AFP led to a large loss of TH activity, whereas a change to the hydrophobic residue valine, which is a better space-filling match, caused only a small loss (7,8). In the stud...

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Section: Methodsmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Type III Antifreeze Protein Structure and Function

Graether

Deluca

Baardsnes

et al. 1999

Journal of Biological Chemistry

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“…The neural networks were constructed using the Stuttgart neural network simulator (SNNS) [11]. The nets constructed possess eight input units (corresponding to the eight variables), two hidden layers with ten and six units and four, respectively, 15 output units.…”

Section: Methodsmentioning

confidence: 99%

Non-parametric classification of protein secondary structures

Ζιντζαράς

Brown²,

Kowald

2006

Computers in Biology and Medicine

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Proteins were classified into their families using a classification tree method which is based on the coefficient of variations of physico-chemical and geometrical properties of the secondary structures of proteins. The tree method uses as splitting criterion the increase in purity when a node is split into two subnodes and the size of the tree is controlled by a threshold level for the improvement of the apparent misclassification rate (AMR) of the tree after each splitting step. The classification tree method seems effective in reproducing similar structural groupings as the method of dynamic programming. For comparison, we also used another two methods: neural networks and support vector machines. We could show that the presented classification tree method performs better in classifying proteins into their families. The presented algorithm might be suitable for a rapid preliminary classification of proteins into their corresponding families.

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“…Some works consider the input function as 978-1-4244-5056-5/09/$26.00 ©2009 IEEE a separated processing taking place in the so called "site" (see [6]). Primarily, the site processing is the cumulative operation between inputs, and the output of the site is simply the summed value of all inputs.…”

Section: A the Site As The Input Function Of The Neuronmentioning

confidence: 99%

Numerical-logical processing in Neural Networks for the decision support

Ariton

2009

2009 3rd International Workshop on Soft Computing Applications

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Artificial Neural Networks (ANN) embed shallow knowledge through learning. Used in diagnosis and decision support, ANN are immediate computational models for effects and causes as from human experience but keep out from the deep knowledge of them. The paper presents a way of embedding logical processing over the numerical ones in "neural logical sites" for the classical ANN paradigms, then proposes a way of structuring deep knowledge in the network for all types of abduction problems in a unified way, which is compared with similar attempt. The approach may be spread in any diagnosis and decision support applications involving deep and shallow knowledge.

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The SNNS Neural Network Simulator

Cited by 13 publications

References 4 publications

Quantitative and Qualitative Analysis of Type III Antifreeze Protein Structure and Function

Quantitative and Qualitative Analysis of Type III Antifreeze Protein Structure and Function

Non-parametric classification of protein secondary structures

Numerical-logical processing in Neural Networks for the decision support

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