SEARCH · Search PubMed

Results for “Neural network”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,747 records · Page 97Linked to original sources

PubMed1999

Semicontinuous cardiac output monitoring using a neural network.

OBJECTIVES: This study compared 2-mL bolus thermodilution cardiac output measurements with standard 10-mL bolus measurements. DESIGN: Cardiac output was measured with the new 2-mL bolus technique and the 10-mL standard thermodilution technique in a perspective series. We describe a system that automatically cools and injects 2-mL boluses of saline into a standard pulmonary artery catheter. It uses a Peltier effect solid-state cooler and pneumatically driven syringe injector to measure cardiac output once per minute. SETTING: Animal laboratory. ANIMALS: Eight adult Duroc swine weighing between 38.0 and 57.5 kg. INTERVENTIONS: Once each minute, 2 mL of cooled 5% dextrose was injected through the pulmonary catheter. Once every 8 mins, four sequential measurements of cardiac output were made using 10-mL injections. MEASUREMENTS AND MAIN RESULTS: A total of 1249 paired waveforms were processed with both a conventional algorithm and with a neural network. For the conventional algorithm, the correlation coefficient was r2 = .92 and the SD of the difference was 1.30 L/min. For the neural network, the correlation coefficient was r2 = .94 and the SD of the difference was 0.88 L/min. Output filtering improved the results in both cases. CONCLUSION: Neural networks accurately derive cardiac output from 2-mL bolus thermodilution injections, allowing cardiac output to be monitored automatically once per minute in many patients. The technique is convenient and uses standard low-cost catheters.

Algorithms↗

PubMed1999

An adaptive Bayesian pruning for neural networks in a non-stationary environment.

Pruning a neural network to a reasonable smaller size, and if possible to give a better generalization, has long been investigated. Conventionally the common technique of pruning is based on considering error sensitivity measure, and the nature of the problem being solved is usually stationary. In this article, we present an adaptive pruning algorithm for use in a nonstationary environment. The idea relies on the use of the extended Kalman filter (EKF) training method. Since EKF is a recursive Bayesian algorithm, we define a weight-importance measure in term of the sensitivity of a posteriori probability. Making use of this new measure and the adaptive nature of EKF, we devise an adaptive pruning algorithm called adaptive Bayesian pruning. Simulation results indicate that in a noisy nonstationary environment, the proposed pruning algorithm is able to remove network redundancy adaptively and yet preserve the same generalization ability.

Bayes Theorem↗

PubMedDate not supplied

Assessment of statistical and neural networks methods in NMR spectral classification and metabolite selection.

Magnetic resonance spectroscopy opens a window into the biochemistry of living tissue. However, spectra acquired from different tissue types in vivo or in vitro and from body fluids contain a large number of peaks from a range of metabolites, whose relative intensities vary substantially and in complicated ways even between successive samples from the same category. The realization of the full clinical potential of NMR spectroscopy relies, in part, on our ability to interpret and quantify the role of individual metabolites in characterizing specific tissue and tissue conditions. This paper addresses the problem of tissue classification by analysing NMR spectra using statistical and neural network methods. It assesses the performance of classification models from a range of statistical methods and compares them with the performance of artificial neural network models. The paper also assesses the consistency of the models in selecting, directly from the spectra, the subsets of metabolites most relevant for differentiating between tissue types. The analysis techniques are examined using in vitro spectra from eight classes of normal tissue and tumours obtained from rats. We show that, for the given data set, the performance of linear and non-linear methods is comparable, possibly due to the small sample size per class. We also show that using a subset of metabolites selected by linear discriminant analysis for further analysis by neural networks improves the classification accuracy, and reduces the number of metabolites necessary for correct classification.

Animals↗

PubMed2005

SNP discovery using advanced algorithms and neural networks.

UNLABELLED: Forage is an application which uses two neural networks for detecting single nucleotide polymorphisms (SNPs). Potential SNP candidates are identified in multiple alignments. Each candidate is then represented by a vector of features, which is classified as SNP or monomorphic by the networks. A validated dataset of SNPs was constructed from experimentally verified SNP data and used for network training and method evalutation. AVAILABILITY: The package is available at biobase.biotech.kth.se/forage/

Algorithms↗

PubMed2004

Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population.

PURPOSE: The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colorectal cancer from healthy individuals. EXPERIMENTAL DESIGN: We subjected serum samples (including 55 colorectal cancer patients and 92 age- and sex-matched healthy individuals) from 147 individuals, for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.0. Using a multilayer artificial neural network with a back propagation algorithm, we developed a classifier for separating the colorectal cancer groups from the healthy groups. RESULTS: The artificial neural network classifier separated the colorectal cancer from the healthy samples, with a sensitivity of 91% and specificity of 93%. Four top-scored peaks, at m/z of 5,911, 8,930, 8,817, and 4,476, were finally selected as the potential "fingerprints" for detection of colorectal cancer. CONCLUSIONS: The combination of SELDI-TOF mass spectrometry with the artificial neural networks in the analysis of serum protein yields significantly higher sensitivity and specificity values for the detection and diagnosis of colorectal cancer.

Adult↗

PubMed2004

Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination.

This paper presents a novel method, named wavelet packet transform based multilayer feedforward neural network with Levenberg-Marquardt and back propagation algorithm (WPTLMBP), developed for simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet packet domain the quality of noise removal can be improved. The artificial neural network was applied for non-linear multivariate calibration. In this study, by optimization, wavelet packet function, decomposition level and number of hidden nodes for WPTLMBP method were selected as Db2, 2, and 4 respectively. A program PWPTLMBP was designed to perform simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). The relative standard error of prediction (RSEP) for all components with WPTLMBP, LM-BP-MLFN, and PLS methods were 6.39, 10.4, and 8.30%, respectively. Experimental results showed the proposed method to be successful and better than the others.

Copper↗

PubMed2004

Exchange of unsaturated fatty acids between adipose tissue and atherosclerotic plaque studied with artificial neural networks.

The linoleic C18:2 (n-6) and linolenic C18:3 (n-3) are recognized as essential components of the diet. Free radical peroxidation of essential fatty acids (EFAs) present in lipoproteins produces oxidized low-density lipoproteins which play a critical role in the development of atherosclerosis. The accumulation of EFAs in the vascular wall and correlations between their content in the adipose tissue and atherosclerotic plaque have been confirmed. The present study was undertaken to determine the usefulness of a neural network for studying the exchange between tissues of linoleic, alpha-linolenic, and arachidonic acids-three fatty acids with a well-understood metabolism. Atheromatous plaques, adipose tissue, and serum were obtained from 31 patients who underwent surgery due to atherosclerotic stenosis of the abdominal aorta, iliac or femoral arteries. Fatty acids were extracted and separated as methyl esters using gas chromatography. Statistical analysis was done with STATISTICA neural networks package. Several correlations reported previously were corroborated and factors modifying the content of individual EFAs in adipose tissue and atherosclerotic plaque were revealed. Artificial neural networks (ANNs) were used to determine factors modifying the content of linoleic, alpha-linolenic, and arachidonic acids in human atheromatous plaques. The mechanism of exchange of some fatty acids between the adipose tissue, atheromatous plaque, and plasma is discussed. The results provide evidence for an effective mechanism of tissue uptake and turnover of linoleic acid. Reduced plasma levels of this acid are compensated by release from adipose tissue and atheromatous plaque. While alpha-linolenic acid is continuously taken up by the plaque, adipose tissue absorbs this acid to a certain level only. The dynamics of exchange of arachidonic acid between adipose tissue and atheromatous plaque reflects a minor role for adipose tissue in determining plaque content of this acid, suggesting that "de novo" synthesis is the chief source of arachidonic acid in plaques.

Adipose Tissue↗

PubMed1998

Neural network based control schemes for flexible-link manipulators: simulations and experiments.

This paper presents simulation and experimental results on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the modified output re-definition approach. The modified output re-definition approach requires only a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the 'feedback-error-learning' approach to learn the inverse dynamics of the flexible manipulator. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the 'output neural network') is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. The performance of the four proposed neural network controllers is illustrated by simulation results for a two-link planar flexible manipulator and by experimental results for a single flexible-link test-bed. The networks are all trained and employed as online controllers and no off-line training is required.

Journal Article↗

PubMed2006

Dimensionality of amino acid space and solvent accessibility prediction with neural networks.

Solvent accessibility prediction from amino acid sequences has been pursued by several researchers. Such a prediction typically starts by transforming the amino acid category (or type) information into numerical representations. All twenty amino acids can be completely and uniquely represented by 20-dimensional vectors. Here, we investigate if the amino acid space defined in this way really requires twenty dimensions. We tried to develop corresponding representations in fewer dimensions. A method for searching optimal codification schema in an arbitrary space using neural networks was developed. The method is used to obtain optimal encoding of amino acids at various levels of dimensionality, and applied to optimize the amino acid codifications for the prediction of the solvent accessibility values of the proteins using feed-forward neural networks. The traditional 20-dimensional codification seems to be redundant in solving the solvent accessibility prediction problem, since a 1-dimensional codification is able to achieve almost the same degree of accuracy as the 20-dimensional codification. Optimal coding in much fewer dimensions could be used to make the predictions of accessible surface area with almost the same degree of accuracy as that obtained by a fully unique 20-dimensional coding. The 1-dimensional amino acid codification for solvent accessibility prediction obtained by a purely mathematical way based on neural networks is highly correlated with a physical property of the amino acids, namely their average solvent accessibility. The method developed to find the optimal codification is general, although the codification thus produced is dependent on the type of estimated property.

Amino Acid Sequence↗

PubMed2004

Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network.

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFEmain performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx, emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

Energy-Generating Resources↗

PubMed2005

Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors. Application to HIV-1 protease inhibitors.

We present here a neural networks method designed to predict biological activity based on a local representation of the ligand. The compounds of the series are represented by a vector mapping for each of four substituent properties: volume, log P, dipole moment and a simple 'steric' parameter relating to its shape. This ligand representation was tested using neural networks on a set of 42 cyclic-urea derivatives, inhibiting HIV-1 protease. The leave-one-out cross-validation using all descriptors in the input gave a correlation factor between prediction and experiment of 0.76 for the overall set and 0.88 when three outliers were left out. To rank the significance of the four descriptors, we further tested all combinations of two and three parameters for each substituent, using two disjunctive testing sets of five inhibitors. In these sets, vectors with extreme descriptor values were used either in the training or the testing set (sets A and B, respectively). The method is a very good interpolator (set A, 95+/-2% accuracy) but a less effective extrapolator (set B, 85+/-2% accuracy). Generally, the combinations including the 'steric' parameter predict better than average, while those containing the volume are less effective. The best prediction, 98.8+/-1.2%, was obtained when log P, the dipole and the steric parameter were used on set A. At the opposite end, the lowest ranked descriptor set was obtained when replacing log P with the volume, giving 92.3+/-6.7% accuracy over the set A.

Combinatorial Chemistry Techniques↗

PubMed1998

Artificial neural network method for predicting HIV protease cleavage sites in protein.

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen's self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.

Amino Acid Sequence↗

PubMed1998

Application of artificial neural networks in HPLC method development.

The use of artificial neural networks (ANNs) for response surface modelling in HPLC method development for amiloride and methychlothiazide separation is reported. The independent input variables were pH and methanol percentage in mobile phase. The outputs were capacity factors. The results were compared with a statistical method (multiple nonlinear regression analysis). Networks were able to predict the experimental responses more accurately than the regression analysis.

Amiloride↗

PubMed2003

A combined neural network and decision trees model for prognosis of breast cancer relapse.

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for breast cancer outcome appear to be significant predictors for overall survival, but probably form part of a bigger picture comprising many factors. Survival estimations are currently performed by clinicians using the statistical techniques of survival analysis. In this sense, artificial neural networks are shown to be a powerful tool for analysing datasets where there are complicated non-linear interactions between the input data and the information to be predicted. This paper presents a decision support tool for the prognosis of breast cancer relapse that combines a novel algorithm TDIDT (control of induction by sample division method, CIDIM), to select the most relevant prognostic factors for the accurate prognosis of breast cancer, with a system composed of different neural networks topologies that takes as input the selected variables in order for it to reach good correct classification probability. In addition, a new method for the estimate of Bayes' optimal error using the neural network paradigm is proposed. Clinical-pathological data were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Málaga, Spain. The results show that the proposed system is an useful tool to be used by clinicians to search through large datasets seeking subtle patterns in prognostic factors, and that may further assist the selection of appropriate adjuvant treatments for the individual patient.

Adult↗

PubMed2000

On the capacity of multilayer neural networks trained with backpropagation.

The capacity of a layered neural network for learning hetero-associations is studied numerically as a function of the number M of hidden neurons. We find that there is a sharp change in the learning ability of the network as the number of hetero-associations increases. This fact allows us to define a maximum capacity C for a given architecture. It is found that C grows logarithmically with M.

Mathematics↗

PubMed2004

Rapid method for prediction of Escherichia coli numbers using an electronic sensor array and an artificial neural network.

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37 degrees C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 10(5) CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography-mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.

Biosensing Techniques↗

PubMed1999

Parameter convergence and learning curves for neural networks.

We revisit the oft-studied asymptotic (in sample size) behavior of the parameter or weight estimate returned by any member of a large family of neural network training algorithms. By properly accounting for the characteristic property of neural networks that their empirical and generalization errors possess multiple minima, we rigorously establish conditions under which the parameter estimate converges strongly into the set of minima of the generalization error. Convergence of the parameter estimate to a particular value cannot be guaranteed under our assumptions. We then evaluate the asymptotic distribution of the distance between the parameter estimate and its nearest neighbor among the set of minima of the generalization error. Results on this question have appeared numerous times and generally assert asymptotic normality, the conclusion expected from familiar statistical arguments concerned with maximum likelihood estimators. These conclusions are usually reached on the basis of somewhat informal calculations, although we shall see that the situation is somewhat delicate. The preceding results then provide a derivation of learning curves for generalization and empirical errors that leads to bounds on rates of convergence.

Algorithms↗

PubMedDate not supplied

Bleeding predisposition assessments in tonsillectomy/adenoidectomy patients using fuzzy interquartile encoded neural networks.

A fuzzy set theoretic methodology is described that serves as a classification preprocessing strategy for supervised feed-forward neural networks. This methodology, fuzzy interquartile encoding, determines the respective degrees to which a feature belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. These membership values are subsequently used in place of the original feature. This transformation has a normalizing effect on the feature space and is more robust to feature outliers. Its effectiveness is scrutinized using several synthetic data sets with various underlying distributions. Fuzzy interquartile encoding is shown to consistently improve the discriminatory power of the underlying classifiers. The methodology is also applied to two biomedical data sets relating to tonsillectomy and/or adenoidectomy patients who may or may not have had a predisposition to excessive bleeding during their operation. The features of the first data set are blood sample test results acquired from a coagulation laboratory and the class labels are one of three hemostatic defects as identified by the reference tests. The second data set consists of patient responses to queries from a bleeding tendency questionnaire. Normal and abnormal class labels were derived from a hematology expert system designed in consultation with a pediatric hematologist. Fuzzy interquartile encoding effected an 11% improvement in the classification accuracy of the underlying neural network classifier with the former data set and 18% with the latter.

Adenoidectomy↗