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At least 73 records · Page 4Linked to original sources
PubMed1991

A neural network model of neural activity in the monkey globus pallidus.

A 3-layer neural network model was constructed to determine the discharge patterns of neurons within the globus pallidus (GP) which would be required to run a sequence of movements at the motor cortical level. The model was based on the presence of tonic and phasic neuronal activity within the motor cortical region; that positive feedback was required to maintain tonic neuronal activity and that phasic neuronal activity was required to initiate and terminate the tonic neuronal activity. The model predicted the presence of both phasic and tonic activity within the middle layer (layer 2; GP) of the model in order for the motor cortical regions (layers 1 and 3) to be able to run and to maintain the movement sequence. This prediction was in keeping with our electrophysiological findings within GP.

Animals↗
PubMed1994

HIV-1 reverse transcriptase inhibitor design using artificial neural networks.

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.

Algorithms↗
PubMed1996

Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.

Animals↗
PubMedDate not supplied

Cancer evolution assessment using artificial neural networks.

Artificial Neural Networks have demonstrated good utility to establish disease prognostic, if there are correctly trained. Our aim was to realize Artificial Neural Networks in different cancer types, to evaluate time survival. For the beginning this was applied for breast cancer. The conclusion is that the homogeneity of data entrance sets, the number, and their coding in relation with their importance for cancer prognostic are decisive for the results of the trained ANN. These results can be useful in the physician clinical decision.

Adult↗
PubMed1995

Identification of a hypoperfused segment in bull's-eye myocardial perfusion images using a feed forward neural network.

Artificial neural networks are computer systems which can be trained to recognize similarities in patterns and which learn by example; one of the more straightforward types being the feed forward neural network (FFNN). We previously reported the use of FFNNs for classification of hypoperfusion patterns in bull's-eye representation of 201Tl single photon emission tomography myocardial perfusion studies and showed that, when such an image was divided into 24 segments, FFNNs could detect perfusion defects without direct comparison to a normal data base. This has been extended in this investigation to assess the ability of an FFNN, trained on data in which only a single segment was hypoperfused, to detect this abnormal segment when the hypoperfusion pattern of the other segments in the image varied. The results indicated that the network could reliably determine whether a segment was normally or under perfused, with accuracies of 99% and 100%, respectively, if all other segments were normally perfused. It could also reliably detect a normally perfused segment, even if other segments were hypoperfused, with accuracies of 95% and 98%. The network was less reliable, however, in detecting a hypoperfused segment when other segments were also hypoperfused, showing accuracies of only 74% and 88%.

Blood Flow Velocity↗
PubMed2004

Patterns of PCDDs and PCDFs in human milk and food and their characterization by artificial neural networks.

Artificial neural network (ANN) has been recently introduced as a tool for data analysis. In this study, Kohonen's self-organizing maps (SOMs), a special type of neural network, were applied to a set of PCDD/PCDF concentrations found in 54 human milk and 83 food samples, which were collected in a number of countries all over the world. Data were obtained from the scientific literature. The purpose of the study was to find a potential relationship between PCDD/PCDF congener profiles in human milk and the dietary habits of the different countries in which samples were collected. The comparison of the SOM component planes for human milk and foodstuffs indicates that those countries with a greater fish consumption show also higher PCDD/PCDF concentrations in human milk. SOMs enable both the visualization of sample units and the visualization of congener distribution.

Algorithms↗
PubMedDate not supplied

An advisory system for artificial ventilation of the newborn utilizing a neural network.

A neural network has been developed to manage ventilated neonates. The network inputs are the current ventilator settings (inspiratory and expiratory times, peak inspiratory and positive end-expiratory pressures and inspired oxygen concentration), partial pressures of arterial blood gases and pH. Two hidden layers comprising 50 nodes each are employed in the network, which utilizes a standard back-propagation algorithm. The network provides the new ventilator settings as five outputs that represent the most appropriate ventilator settings projected to maintain blood gases within an acceptable range. The network has been trained using a data set derived from a rule-based expert system developed for the same purpose. Performances of both systems have been compared. The neural network is capable of learning and adapting to the individual patient's response, which in principle offers significant advantages over the rule-based system.

Blood Gas Analysis↗
PubMed1997

Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks.

A neural network approach is proposed for the automated classification of the normal and abnormal EGG. Two learning algorithms, the quasi-Newton and the scaled conjugate gradient method for the multilayer feedforward neural networks (MFNN), are introduced and compared with the error backpropagation algorithm. The configurations of the MFNN are determined by experiment. The raw EGG data, its power spectral data, and its autoregressive moving average (ARMA) modelling parameters are used as the input to the MFNN and compared with each other. Three indexes (the percent correct, sum-squared error and complexity per iteration) are used to evaluate the performance of each learning algorithm. The results show that the scaled conjugate gradient algorithm performs best, in that it is robust and provides a super-linear convergence rate. The power spectral representation and the ARMA modelling parameters of the EGG are found to be better types of the input to the network for this specific application, both yielding a percent correctness of 95% on the test set. Although the results are focused on the classification of the EGG, this paper should provide useful information for the classification of other biomedical signals.

Algorithms↗
PubMed1996

Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks.

Artificial neural networks can be used to recognize lead reversals in the 12-lead electrocardiogram at very high specificity, and the sensitivity is much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of lead reversals, in combination with an algorithm for the right arm/right foot lead reversal, would recognize approximately 75% of lead reversals encountered in clinical practice.

Electrocardiography↗
PubMed2001

Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network.

A neural network system was designed to extract and analyze the quantitative data from time-intensity profile. These data was used to predict the outcome of biopsy in a group of patients with histopathologically proved breast lesions. The performance of an artificial neural network (ANN) was compared with radiologists using a database with 120 patients' records each of which consisted of 14 quantitative parameters mostly derived directly from time-intensity profile. The network was trained and tested using the jackknife method and its performance was then compared with that of the radiologists in terms of sensitivity, specificity and accuracy using receiver operating characteristic curve (ROC) analysis. The network was able to classify correctly 107 of 120 original cases and yielded a better diagnostic accuracy (89%), compared with that of the radiologist (79%) by performing a constructive association between extracted quantitative data and corresponding pathological results (r=0.72, P<0.001).

Adolescent↗
PubMed1996

The importance of larger data sets for protein secondary structure prediction with neural networks.

A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer. In a 32-fold cross validated test, secondary structure prediction accuracy is 67.0%, relative to 62.6% obtained previously, without any evolutionary information on the sequence. Introduction of sequence profiles increases this value to 72.9%, suggesting that the two types of information are essentially independent. Tertiary structural class is predicted with 80.2% accuracy, relative to 73.9% obtained previously. The use of a larger database is facilitated by the introduction of a scaled conjugate gradient algorithm for optimizing the neural network. This algorithm is about 10-20 times as fast as the standard steepest descent algorithm.

Algorithms↗
PubMed2003

Prediction of protein secondary structure content by artificial neural network.

The neural network method was applied to the prediction of the content of protein secondary structure elements, including alpha-helix, beta-strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil. The "pair-coupled amino acid composition" originally proposed by K. C. Chou [J Protein Chem 1999, 18, 473] was adopted as the input. Self-consistency and independent-dataset tests were used to appraise the performance of the neural network. Results of both tests indicated high performance of the method.

Algorithms↗
PubMed2002

A methodology to explain neural network classification.

Neural networks are still frustrating tools in the data mining arsenal. They exhibit excellent modelling performance, but do not give a clue about the structure of their models. We propose a methodology to explain the classification obtained by a multilayer perceptron. We introduce the concept of 'causal importance' and define a saliency measurement allowing the selection of relevant variables. Once the model is trained with the relevant variables only, we define a clustering of the data built from the hidden layer representation. Combining the saliency and the causal importance on a cluster by cluster basis allows an interpretation of the neural network classifier to be built. We illustrate the performances of this methodology on three benchmark datasets.

Classification↗
PubMed1991

Novel method for the display of multivariate data using neural networks.

A neural network has been used to reduce the dimensionality of multivariate data sets to produce two-dimensional (2D) displays of these sets. The data consisted of physicochemical properties for sets of biologically active molecules calculated by computational chemistry methods. Previous work has demonstrated that these data contain sufficient relevant information to classify the compounds according to their biological activity. The plots produced by the neural network are compared with results from two other techniques for linear and nonlinear dimension reduction, and are shown to give comparable and, in one case, superior results. Advantages of this technique are discussed.

Antimycin A↗
PubMed2005

Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network.

Artificial neural network (ANN) implementing the back-propagation algorithm was applied for the calculation of the imprinting factors (IF) of molecularly imprinted polymers (MIP) as a function of the computed molecular descriptors of template and functional monomer molecules and mobile phase descriptors. The dataset used in our study were obtained from the literature and classified into two distinctive datasets on the basis of the polymer's morphology, irregularly sized MIP and uniformly sized MIP datasets. Results revealed that artificial neural network was able to perform well on datasets derived from uniformly sized MIP (n = 23, r = 0.946, RMS = 2.944) while performing poorly on datasets derived from irregularly sized MIP (n = 75, r = 0.382, RMS = 6.123). The superior performance of the uniformly sized MIP dataset over the irregularly sized MIP dataset could be attributed to its more predictable nature owing to the consistency of MIP particles, uniform number and association constant of binding sites, and minimal deviation of the imprinted polymers. The ability to predict the imprinting factor of imprinted polymer prior to performing actual experimental work provide great insights on the feasibility of the interaction between template-functional monomer pairs.

Neural Networks, Computer↗