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At least 217 records · Page 12Linked to original sources
PubMed2005

Modeling the electrophoretic mobility of analytes in binary solvent electrolyte systems in capillary electrophoresis using an artificial neural network.

An artificial neural network (ANN) methodology was used to model the electrophoretic mobility of basic analytes in binary solvent electrolyte systems. The electrophoretic mobilities in pure solvent electrolytes, and the volume fractions of the solvents in mixtures were used as input. The electrophoretic mobilities in mixed solvent buffers were employed as the output of the network. The optimized topology of the network was 3-3-1. 32 experimental mobility data sets collected from the literature were employed to test the correlation ability and prediction capability of the proposed method. The mean percentage deviation (MPD) between the experimental and calculated values was used as an accuracy criterion. The MPDs obtained for different numerical analyses varied between 0.21% and 13.74%. The results were also compared with similar calculated mobilities which were derived from the best multiple linear model from the literature. From these results it was found that the ANN methodology is superior to the multiple linear model.

Adrenergic beta-Antagonists↗
PubMed2005

What are artificial neural networks and what they can do?

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.

Algorithms↗
PubMed2005

High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network.

An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

Algorithms↗
PubMed2005

Prediction of human intestinal absorption using an artificial neural network.

An artificial neural network model is developed to predict percent human intestinal absorption (%FA) of compounds from their molecular structural parameters. These parameters are the polar molecular surface area (PSA), the fraction of polar molecular surface area (FPSA, polar molecular surface area/ molecular surface area), the sum of the net atomic charges of oxygen atoms (Q(O)), the sum of the net atomic charges of nitrogen atoms with net negative atomic charges (Q(N)), the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)), and the number of carboxyls (nCOOH). For a training set of 85 compounds anda test set of 10 compounds, root mean squared errors (RMSE) between experimental %FA valuesand calculated/predicted %FA values are 8.86% and 14.1%, respectively.

Artificial Intelligence↗
PubMed2001

A selective learning method to improve the generalization of multilayer feedforward neural networks.

Multilayer feedforward neural networks with backpropagation algorithm have been used successfully in many applications. However, the level of generalization is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be predicted. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domains: two artificial approximation problems and a real time series prediction problem. Results have been compared to standard backpropagation using the complete training data set and the new method shows better generalization abilities.

Forecasting↗
PubMed2004

Predicting blood-brain barrier penetration of drugs using an artificial neural network.

An artificial neural network model is developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (log BB) from their molecular structural parameters. These molecular structural parameters are the molecular volume (V), the sum of the absolute values of the net atomic charges of oxygen and nitrogen atoms which are hydrogen-bond acceptors (Q(O, N)), and the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)). For a training set of 56 compounds and a test set of 5 compounds, root mean squared errors (RMSE) between experimental log BB values and calculated/predicted log BB values were 0.236 and 0.258, respectively. These molecular structural parameters can be obtained easily from quantum chemical calculations. The model is suitable for the rapid prediction of the blood-brain barrier penetration of drugs.

Algorithms↗
PubMed2003

Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network.

A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals, ditches, lakes, and pools in The Netherlands. By training the CPN, the sampling sites were classified into five groups and the classification was mainly related to pollution status and habitat type of the sampling sites. By visualizing environmental variables and diversity indices on the map of the trained model, the relationships between variables were evaluated. The trained CPN serves as a 'look-up table' for finding the corresponding values between environmental variables and community indices. The output of the model fitted SH and SR well showing a high accuracy of the prediction (r>0.90 and 0.67 for learning and testing process, respectively) for both SH and SR. Finally, the results of this study, which uses the capability of the CPN for patterning and predicting ecological data, suggest that the CPN can be effectively used as a tool for assessing ecological status and predicting water quality of target ecosystems.

Animals↗
PubMed1998

Psychopharmacology of latent inhibition: a neural network approach.

A neural network model of classical conditioning is applied to the description of some aspects of the psychopharmacology of latent inhibition (LI). According to the model, LI is manifested because preexposure of the conditioned stimulus (CS) reduces Novelty, defined as proportional to the sum of the mismatches between predicted and observed events, thereby reducing attention to the CS and retarding conditioning. In the framework of the model, it is assumed that indirect dopaminergic (DA) agonists (e.g. amphetamine and nicotine) increase, and DA receptor antagonists (e.g. haloperidol and alpha-flupenthixol) decrease, the effect of Novelty on attention. Computer simulations demonstrate that, under these assumptions, the model correctly describes: (1) the impairment of LI by amphetamine when a strong unconditioned stimulus (US) is used, (2) the impairment of LI by amphetamine when a nonsalient CS is used, (3) the impairment of LI by amphetamine administration when a short CS is used, (4) the facilitation of LI by alpha-flupenthixol when a weak US is used, (5) the facilitation of LI by haloperidol when a nonsalient CS is used, (6) the facilitation of LI by haloperidol with a strong US, and (7) the facilitation of LI by haloperidol with extended conditioning.

Amphetamine↗
PubMed1997

Ground reaction forces in horses, assessed from hoof wall deformation using artificial neural networks.

An artificial neural network (ANN) was developed to investigate whether hoof wall deformation could be used to determine ground reaction forces (GRF) in horses. The ANN was taught this relationship under certain conditions and was able to generalise this knowledge to conditions for which it was not trained before. To acquire data to train and test the ANN, a horse was equipped with strain gauges attached to the dorsal, lateral and medial parts of the hoof to assess hoof wall deformation. A force plate was used to measure the GRFs. Both hoof wall deformation and GRF were recorded simultaneously at different speeds, gaits, surfaces and loads. An ANN was trained with some of these data, and subsequently provided with strain gauge recordings of strides, not used for training. By comparing the GRF calculated by the ANN based on the hoof wall deformation with that recorded simultaneously by the force plate, the generalisability of the ANN was determined. It was found that an ANN is capable of 'learning' the relationship between hoof wall deformation and GRF, and to generalise it to a wide range of new conditions. This technique enables assessment of GRF under difficult conditions, such as on a treadmill or on surfaces where a force plate cannot be employed.

Animals↗
PubMed2005

Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks.

Using artificial neural networks (ANNs), a highly accurate model was developed to simulate survival curves of Listeria monocytogenes in chorizos as affected by the initial water activity (a(w0)) of the sausage formulation, temperature (T), and air inflow velocity (F) where the sausages are stored. The ANN-based survival model (R(2)=0.970) outperformed the regression-based cubic model (R(2)=0.851), and as such was used to derive other models (using regression) that allow prediction of the times needed to drop count by 1, 2, 3, and 4 logs (i.e., nD-values, n=1, 2, 3, 4). The nD-value regression models almost perfectly predicted the various times derived from a number of simulated survival curves exhibiting a wide variety of the operating conditions (R(2)=0.990-0.995). The nD-values were found to decrease with decreasing a(w0), and increasing T and F. The influence of a(w0) on nD-values seems to become more significant at some critical value of a(w0), below which the variation is negligible (0.93 for 1D-value, 0.90 for 2D-value, and <0.85 for 3D- and 4D-values). There is greater influence of storage T and F on 3D- and 4D-values than on 1D- and 2D-values.

Animals↗
PubMed2003

Predicting the intracellular water compartment using artificial neural network analysis.

Artificial neural networks (ANN) are used for a wide variety of data-processing applications such as predicting medical outcomes and classifying clinical data and patients. We investigated the applicability of an ANN for estimating the intracellular water compartment for a population of 104 healthy Italians ranging in age from 19 to 68 years. Anthropometric variables, bioelectric impedance analysis (BIA) variables, and reference values for intracellular water, measured using whole-body (40)K counting (ICW(K40)), were measured for all study participants. The anthropometric variables and the impedance index (height(2)/resistance) were fed to the ANN input layer, which produced as output the estimated values for intracellular water (ICW(ANN)). We also estimated intracellular water using a BIA formula for the same population (ICW(DeLorenzo)) and another for Caucasians (ICW(Gudivaka)). Errors in the estimations generated by ANN and the BIA equations were calculated as the root mean square error (RMSE). The mean (+/-SD) reference value (ICWK40) was 25.01+/-4.50 l, whereas the mean estimated value was 15.20+/-1.79 l (RMSE=11.06 l) when calculated using ICW(DeLorenzo), 18.07+/-1.14 l (RMSE=8.72 l) when using ICW(Gudivaka), and 25.01+/-2.74 l (RMSE=3.22 l) when using ICW(ANN). Based on these results, we deduce that the ANN algorithm is a more accurate predictor for reference ICW(K40) than BIA equations.

Adult↗
PubMed1997

Prediction of dynamic tendon forces from electromyographic signals: an artificial neural network approach.

Artificial neural networks (ANN) with a backpropagation algorithm were used to predict dynamic tendon forces from electromyographic (EMG) signals. To achieve this goal, tendon forces and EMG-signals were recorded simultaneously in the gastrocnemius muscle of three cats while walking and trotting at different speeds on a motor-driven treadmill. The quality of the tendon force predictions were evaluated for three levels of generalization. First, at the intrasession level, tendon force predictions were made for step cycles from the same experimental session as the step cycles which were used to train the ANN. At this level of generalization very good results were obtained. Second, at the intrasubject level, tendon force predictions were made for one cat walking at a given speed while the ANN was trained with data from the same animal walking at different speeds. For the intrasubject predictions, the quality of the results depended on the walking speed for which the predictions were made: for the speeds at the low and high extremes, the predictions were worse than for the intermediate speeds. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.78 to 0.91. Third, at the intersubject level, tendon forces were predicted for one animal walking at a given speed while the ANN was trained with data from the remaining two animals walking at the corresponding speed. The cross-correlation coefficients between predicted and actual force time histories ranged from 0.72 to 0.98. It was concluded that the ANN-approach is a powerful technique to predict dynamic tendon forces from EMG-signals.

Animals↗
PubMed2005

Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis.

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log kp) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r2=0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r2=0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.

Algorithms↗
PubMed1995

QSAR of 3-methylfentanyl derivatives studied with neural networks method.

AIM: To use neural networks, which simulate the functions of living nervous systems, in QSAR studies; METHODS: Using the back-propagation neural networks program devised by us, combining with partial least squares (PLS) method, we studied the relationships of quantum chemical indices and analgesic activities of 25 3-methylfentanyl derivatives; RESULTS: Through learning process, a good QSAR model was established, and the activities of these compounds were predicted; the correlation between the activities and quantum chemical indices: the net charge of the atom N1, the net charge of the atom O16, the torsional angle of atoms C10-C9-N8-C4, the interatomic distance between atom C7 and the center of phenyl plane C9-14 (PhA), is quite well-matched. Based on these results, an interactive pattern between 3-methylfentanyl derivatives and opioid receptors was suggested; CONCLUSION: Not only are the results of neural networks superior to those of PLS method but they also provide accurate predictions of the activity of the compounds and also combine the PLS method with neural networks.

Analgesics↗
PubMedDate not supplied

Prediction of community-acquired pneumonia using artificial neural networks.

BACKGROUND: Artificial neural networks (ANN) have been used in the prediction of several medical conditions but have not been previously used to predict pneumonia. The authors used ANN to predict the presence or absence of pneumonia among patients presenting to the emergency department with acute respiratory complaints and compared the results with those obtained using logistic regression modeling. METHODS: Feed-forward back-propagation ANN were trained on sociodemographic, symptom, sign, comorbidity, and radiographic outcome data among 1,044 patients from the University of Illinois (the training cohort) and were applied to 116 patients from the University of Nebraska (the testing cohort). ANN trained using different strategies were compared to each other and to main-effects logistic regression. Calibration accuracy was measured as mean square error and discrimination accuracy as the area under a receiver operating characteristic (ROC) curve. RESULTS: A 1 hidden-layer ANN trained using oversampling of pneumonia cases had an ROC area in the training cohort of 0.895, which was greater than the area of 0.840 for logistic regression (P = 0.026). This ANN had an ROC area in the testing cohort of 0.872, not significantly different from its area in the training cohort (P = 0.597). Operating at a threshold of 0.25, the ANN would have detected 94% to 95% of patients with pneumonia in the 2 cohorts while correctly excluding 39% to 50% of patients with other conditions. ANN trained using other strategies discriminated equally in the 2 cohorts but no better than did logistic regression. CONCLUSIONS: Among adults presenting with acute respiratory illness, ANN accurately discriminated patients with and without pneumonia and, under some circumstances, improved on the accuracy of logistic regression.

Adult↗
PubMed2003

Optimization of a fermentation medium using neural networks and genetic algorithms.

Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.

Algorithms↗
PubMedDate not supplied

Application of four-layer neural network on information extraction.

This paper applies neural network to extract marsh information. An adaptive back-propagation algorithm based on a robust error function is introduced to build a four-layer neural network, and it is used to classify Thematic Mapper (TM) image of Zhalong Wetland in China and then extract marsh information. Comparing marsh information extraction results of the four-layer neural network with three-layer neural network and the maximum likelihood classifier, conclusion can be drawn as follows: the structure of the four-layer neural network and the adaptive back-propagation algorithm based on the robust error function is effective to extract marsh information. The four-layer neural network adopted in this paper succeeded in building the complex model of TM image, and it avoided the problem of great storage of remotely sensed data, and the adaptive back-propagation algorithm speeded up the descending of error. Above all, the four-layer neural network is superior to the three-layer neural network and the maximum likelihood classifier in the accuracy of the total classification and marsh information extraction.

Algorithms↗
PubMed2001

Neural representation of alpha-oriented moving light bars in the cortex: a neural network study.

A neural computational model is suggested in this paper for investigating the stimulus dependence of spiking patterns and the neural representation of alpha-oriented moving light bars in the cortex. In this model, a stimulus-directed cortical developing algorithm is introduced for training the neural network. Three classes of computer simulations concerned with the orientation of the stimulus are carried out. The simulation results show that the fine temporal structure of spiking patterns of single units depends on the alpha orientation of the two moving light bars, and the fine temporal structure of their combinatorial spiking patterns are also context dependent. They also show that the neural representation of an alpha-oriented moving light bar is determined not only by the stimulus itself but also the architecture of the matured network. In the end, we propose a possible neural coding mechanism underlying the temporal cell subassemblies in the cortex, which could be spontaneously and dynamically organized into a dynamical cell assembly by synchronized activity of these subassemblies.

Algorithms↗