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PubMed1991

Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees.

Successful applications of neural network architecture have been described in various fields of science and technology. We have applied one such technique, error back-propagation, to a medical classification problem stemming from clinical chemistry, and we have compared the performance of two different neural networks with results obtained by conventional linear discriminant analysis or by the technique of classification and regression trees. The results obtained by the various models were tested for robustness by jackknife validation ("leave n out" method). Compared with the two other techniques, neural networks show a unique ability to detect features hidden in the input data which are not explicitly formulated as input. Thus, neural network techniques appear promising in the field of clinical chemistry, and their application, particularly in situations with complex data structures, should be investigated with more emphasis.

Alanine Transaminase↗
PubMed2005

Application of remote monitoring and automatic control system using neural network for small wastewater treatment plants in Korea.

For this study, an automatic control system has been developed by using a neural network and internet-based remote monitoring system for efficient operation of plants that have a serious variance of influent loading and have difficulties in appropriate maintenance, just like small wastewater treatment plants in Korea. In the control algorithm, ORP was used as the main sensor for control. At the point where the ORP value was judged to reach the "nitrate knee" of denitrification and phosphorus release, ORP indicated the state of lower saturation read by the neural network and then changed the operating condition from the reduction state to the oxidation state. For example, if ORP indicates the state of higher saturation at the point of "nitrogen breakpoint" or "ammonia valley" of nitrification, the neural network reads it and cuts off the oxygen supply and mixing. The dORP data have been used as one of the main input for the neural network. After the operation of lab-scale cyclic aeration process using an automatic control system, it has been found that regardless of loading variance, more than 95% of organic matters and more than 60% of nitrogen and phosphorus have been removed. Assuming that an internet-connected computer and a basic web browser are available, this study has developed a remote monitoring system that can monitor the operating status of small plants or any troubles with them.

Automation↗
PubMed2004

A hybrid neural network model for noisy data regression.

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Journal Article↗
PubMed2004

The effectiveness of artificial neural networks in evaluating treatment plans for patients requiring external beam radiotherapy.

This study was designed to determine the ability of a neural network to use data summarized in artificial generated dose volume histograms (DVH) for the rectum to evaluate and compare different patient treatment plans for the treatment of localized prostate cancer. One radiotherapist evaluated 250 artificially generated DVHs representing the distribution of a dose of ration throughout the rectum during external radiotherapy for patients with prostatic adenocarcinoma (PAC). The data were also analyzed using the Lyman NTCP-model for assessing complication probabilities. A neural network consisting of 10 input nodes and one output node was trained to categorize the plans according to the radiotherapist's score. The volume in each isodose was used as input, and the risk for a severe complication was presented as output. The classifications made by the neural network matched those determined by the radiotherapist and the NTCP-model. All three techniques showed a high correlation between each other. Artificially generated dose volume histograms (DVH) for the rectum can be used for training a neural network for scoring rectal DVHs in treatment plans for localized prostate cancer.

Adenocarcinoma↗
PubMed2005

Some generalized sufficient convergence criteria for nonlinear continuous neural networks.

A reason for applying the direct method of Lyapunov to artificial neural networks (ANNs) is to design dynamical neural networks so that they exhibit global asymptotic stability. Lyapunov functions that frequently appear in the ANN literature include the quadratic function, the Persidskii function, and the Luré-Postnikov function. This contribution revisits the quadratic function and shows that via Krasovskii-like stability criteria, it is possible to have a very simple and systematic procedure to obtain not only new and generalized results but also well-known sufficient conditions for convergence established recently by non-Lyapunov methods, such as the matrix measure and nonlinear measure.

Algorithms↗
PubMed1994

Neural-network contributions in biotechnology.

In the mid-1980s, widespread interest in research into artificial neural networks re-emerged following a period of reduced research funding. The much wider availability and the increased power of computing systems, together with new areas of research, is expanding the range of potential application. The main reason for this is that the potential to describe the characteristics of extremely complex systems accurately has been attributed to this methodology. This article examines the contribution of various network methodologies to bioprocess modelling, control and pattern recognition. Industrial processes can benefit from the application of feedforward networks with sigmoidal activation functions, radial basis function networks and autoassociative networks. The contribution that neural networks can make to biochemical and microbiological scientific research is also reviewed briefly.

Animals↗
PubMed2002

Artificial neural networks for diagnosis and prognosis in prostate cancer.

The application of artificial neural networks (ANNs), especially feed-forward neural networks (FFNNs), has become very popular for diagnosis and prognosis in clinical medicine, often accompanied by exaggerated statements of their potential. The excitement stems mainly from the fact that ANNs were developed as attempts to model the decision process of the human brain. Traditionally, logistic regression models and proportional hazard regression models have been used in these applications. In this article, FFNNs are introduced as flexible, nonlinear regression models and necessary precautions for their use are discussed. Furthermore, the results of a literature survey of applications of ANNs in prostate cancer published between 1999 and 2001 are described; most applications suffer from methodologic deficiencies. It is concluded that there is so far no evidence that the application of ANNs provide real progress in the field of diagnosis and prognosis in prostate cancer.

Diagnostic Techniques and Procedures↗
PubMed1996

Neural networks and fuzzy logic in clinical laboratory computing with application to integrated monitoring.

We present an analysis of the computational features of neural networks and fuzzy logic architectures which attempts to explain their recent popularity as well as their drawbacks. Based upon many reports in several fields, we identify the key computational requirements in the clinical laboratory setting, and review several classical tools. In particular we make the observation that all of these needs may be viewed as a search for an appropriate mathematical mapping. We suggest that the neural networks promise as a universal function approximant is the main source of its apparent attractivity. We then describe a customized neural network architecture as a non-linear, adaptive signal processor for integrated monitoring. This architecture is employed in the Adaptive Real-Time Anesthesiologist Associate (ARTAA) system, which has been developed as a joint project at the Department of Anesthesiology, Albert Einstein Medical Center and the Electrical and Computer Engineering Department, Drexel University in Philadelphia, USA. In this application the neural network realizes a non-linear scalar map from the set of physiological signals to a vital function status (VFS) indicator. The system is now under clinical testing.

Anesthesia↗
PubMed2006

Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network.

In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, C alpha-H...O and C alpha-H...pi interactions in proteins using artificial neural network. Both standard feed-forward neural network (FNN) and recurrent neural networks (RNN) have been trained and tested using five-fold cross-validation on a non-homologous dataset of 2298 protein chains where no pair of sequences has more than 25% sequence identity. It has been found that the prediction accuracy varies with the separation distance between donor and acceptor residues. The maximum sensitivity achieved with RNN for C alpha-H...O is 51.2% when donor and acceptor residues are four residues apart (i.e. at delta D-A = 4) and for C alpha-H...pi is 82.1% at delta D-A = 3. The performance of RNN is increased by 1-3% for both types of interactions when PSIPRED predicted protein secondary structure is used. Overall, RNN performs better than feed-forward networks at all separation distances between donor-acceptor pair for both types of interactions. Based on the observations, a web server CHpredict (available at http://www.imtech.res.in/raghava/chpredict/) has been developed for predicting donor and acceptor residues in C alpha-H...O and C alpha-H...pi interactions in proteins.

Algorithms↗
PubMedDate not supplied

Analysis of the internal representations developed by neural networks for structures applied to quantitative structure--activity relationship studies of benzodiazepines.

An application of recursive cascade correlation (CC) neural networks to quantitative structure-activity relationship (QSAR) studies is presented, with emphasis on the study of the internal representations developed by the neural networks. Recursive CC is a neural network model recently proposed for the processing of structured data. It allows the direct handling of chemical compounds as labeled ordered directed graphs, and constitutes a novel approach to QSAR. The adopted representation of molecular structure captures, in a quite general and flexible way, significant topological aspects and chemical functionalities for each specific class of molecules showing a particular chemical reactivity or biological activity. A class of 1,4-benzodiazepin-2-ones is analyzed by the proposed approach. It compares favorably versus the traditional QSAR treatment based on equations. To show the ability of the model in capturing most of the structural features that account for the biological activity, the internal representations developed by the networks are analyzed by principal component analysis. This analysis shows that the networks are able to discover relevant structural features just on the basis of the association between the molecular morphology and the target property (affinity).

Benzodiazepines↗
PubMed1999

An hierarchical artificial neural network system for the classification of transmembrane proteins.

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2

Animals↗
PubMed2006

Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feed-forward neural networks.

The reaction dynamics of vibrationally excited vinyl bromide have been investigated using classical trajectory methods on a neural network potential surface that is fitted to an ab initio database of 12 122 configuration energies obtained from electronic structure calculations conducted at the MP4(SDQ) level of theory using a 6-31G(d,p) basis set for the carbon and hydrogen atoms and Huzinaga's (43334334) basis set augmented with split outer s and p orbitals (4332143214) and a polarization f orbital with an exponent of 0.5 for the bromine atom. The sampling of the 12-dimensional configuration hyperspace of vinyl bromide prior to execution of the electronic structure calculations is accomplished by combining novelty-sampling methods, chemical intuition, and trajectory sampling on empirical and neural network surfaces. The final potential is obtained using a two-layer feed-forward neural network comprising 38 and 1 neurons, respectively, with hyperbolic tangent sigmoid and linear transfer functions in the hidden and output layers, respectively. The fitting is accomplished using the Levenberg-Marquardt algorithm with early stopping and Bayesian regularization methods to avoid overfitting. The interpolated potentials have a standard deviation from the ab initio results of 0.0578 eV, which is within the range generally regarded as "chemical accuracy" for the purposes of electronic structure calculations. It is shown that the potential surface may be easily and conveniently transferred from one research group to another. The files required for transfer of the vinyl bromide surface can be obtained from the Electronic Physics Auxiliary Publication Service. Total dissociation rate coefficients for vinyl bromide are obtained at five different excitation energies between 4.50 and 6.44 eV. Branching ratios into each of the six open reaction channels are computed at 24 vibrational energies in the range between 4.00 and 6.44 eV. The distribution of vibrational energies in HBr formed via three-center dissociation from vinyl bromide is determined and compared with previous theoretical and experimental results. It is concluded that the combination of ab initio electronic structure calculations, novelty sampling with chemical intuition and trajectories on empirical analytic surfaces, and feed-forward neural networks provides a viable framework in which to execute purely ab initio molecular-dynamics studies on complex systems with multiple open reaction channels.

Journal Article↗
PubMed1999

Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.

PURPOSE: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. MATERIALS AND METHODS: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an analog video signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. RESULTS: The accuracy of neural networks for classifying malignancies was 95.0% (133 of 140 tumors), the sensitivity was 98% (51 of 52), the specificity was 93% (82 of 88), the positive predictive value was 89% (51 of 57), and the negative predictive value was 99% (82 of 83). CONCLUSION: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses. Because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.

Adolescent↗
PubMed2002

Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks).

In this paper we present a cooperative coevolutive model for the evolution of neural network topology and weights, called MOBNET. MOBNET evolves subcomponents that must be combined in order to form a network, instead of whole networks. The problem of assigning credit to the subcomponents is approached as a multi-objective optimization task. The subcomponents in a cooperative coevolutive model must fulfill different criteria to be useful, these criteria usually conflict with each other. The problem of evaluating the fitness on an individual based on many criteria that must be optimized together can be approached as a multi-criteria optimization problems, so the methods from multi-objective optimization offer the most natural way to solve the problem. In this work we show how using several objectives for every subcomponent and evaluating its fitness as a multi-objective optimization problem, the performance of the model is highly competitive. MOBNET is compared with several standard methods of classification and with other neural network models in solving four real-world problems, and it shows the best overall performance of all classification methods applied. It also produces smaller networks when compared to other models. The basic idea underlying MOBNET is extensible to a more general model of coevolutionary computation, as none of its features are exclusive of neural networks design. There are many applications of cooperative coevolution that could benefit from the multi-objective optimization approach proposed in this paper.

Biological Evolution↗
PubMed2007

Image analysis by pulse coupled neural networks (PCNN)--a novel approach in granule size characterization.

A biologically inspired spiking neural network model, the pulse coupled neural network (PCNN), has been applied for the first time in bulk particle characterization, and specifically in the characterization of pharmaceutical granule size distributions. The PCNN was trained on surface images of pharmaceutical granule beds, and the adjustable parameters (radius neuron interconnection, r0, linking weight coefficient, beta, local threshold potential, VTheta, and number of iterations) were successfully optimized using design of experiments. As demonstrated with size fractions of granules, it was found that the PCNN produced granule size-dependent signals. In general, a first highest and relatively narrow peak located in the region of two to twelve iterations corresponded to smaller particle size, while larger particles resulted in wider peaks and in highest (not first) peak at a range between 13 and 25 iterations. Better predictions, i.e. lower RMSEP (root mean squared error of prediction) values, were obtained using high beta value, low r0 and VTheta values, while the number of iterations had to exceed 110 and the optimized model (RMSEP lower than 5) corresponded to PCNN variables: r0=1, beta=0.4, VTheta=2, and number of iterations=150. The coefficient of determination (R2) of the model was 0.94 and the predicted variation (Q2) was 0.91, while the Pearson correlation coefficient between the predicted and the measured mean particle size by sieving for eight test batches was 0.98. These findings could be characterized as promising and encouraging for the further use of image analysis by PCNNs in pharmaceutical bulk particle size and shape characterization.

Image Processing, Computer-Assisted↗
PubMed2002

Neural network pruning with Tukey-Kramer multiple comparison procedure.

Reducing a neural network's complexity improves the ability of the network to generalize future examples. Like an overfitted regression function, neural networks may miss their target because of the excessive degrees of freedom stored up in unnecessary parameters. Over the past decade, the subject of pruning networks produced nonstatistical algorithms like Skeletonization, Optimal Brain Damage, and Optimal Brain Surgeon as methods to remove connections with the least salience. The method proposed here uses the bootstrap algorithm to estimate the distribution of the model parameter saliences. Statistical multiple comparison procedures are then used to make pruning decisions. We show this method compares well with Optimal Brain Surgeon in terms of ability to prune and the resulting network performance.

Algorithms↗
PubMed2007

Sequence and structural features of carbohydrate binding in proteins and assessment of predictability using a neural network.

BACKGROUND: Protein-Carbohydrate interactions are crucial in many biological processes with implications to drug targeting and gene expression. Nature of protein-carbohydrate interactions may be studied at individual residue level by analyzing local sequence and structure environments in binding regions in comparison to non-binding regions, which provide an inherent control for such analyses. With an ultimate aim of predicting binding sites from sequence and structure, overall statistics of binding regions needs to be compiled. Sequence-based predictions of binding sites have been successfully applied to DNA-binding proteins in our earlier works. We aim to apply similar analysis to carbohydrate binding proteins. However, due to a relatively much smaller region of proteins taking part in such interactions, the methodology and results are significantly different. A comparison of protein-carbohydrate complexes has also been made with other protein-ligand complexes. RESULTS: We have compiled statistics of amino acid compositions in binding versus non-binding regions- general as well as in each different secondary structure conformation. Binding propensities of each of the 20 residue types and their structure features such as solvent accessibility, packing density and secondary structure have been calculated to assess their predisposition to carbohydrate interactions. Finally, evolutionary profiles of amino acid sequences have been used to predict binding sites using a neural network. Another set of neural networks was trained using information from single sequences and the prediction performance from the evolutionary profiles and single sequences were compared. Best of the neural network based prediction could achieve an 87% sensitivity of prediction at 23% specificity for all carbohydrate-binding sites, using evolutionary information. Single sequences gave 68% sensitivity and 55% specificity for the same data set. Sensitivity and specificity for a limited galactose binding data set were obtained as 63% and 79% respectively for evolutionary information and 62% and 68% sensitivity and specificity for single sequences. Propensity and other sequence and structural features of carbohydrate binding sites have also been compared with our similar extensive studies on DNA-binding proteins and also with protein-ligand complexes. CONCLUSION: Carbohydrates typically show a preference to bind aromatic residues and most prominently tryptophan. Higher exposed surface area of binding sites indicates a role of hydrophobic interactions. Neural networks give a moderate success of prediction, which is expected to improve when structures of more protein-carbohydrate complexes become available in future.

Amino Acid Sequence↗
PubMed2005

Evaluation of a generalized regression artificial neural network for extending cadmium's working calibration range in graphite furnace atomic absorption spectrometry.

A generalized regression artificial neural network (GRANN) was developed and evaluated for modeling cadmium's nonlinear calibration curve in order to extend its upper concentration limit from 4.0 microg L-1 up to 22.0 microg L-1. This type of neural network presents important advantages over the more popular backpropagation counterpart which are worth exploiting in analytical applications, namely, (1) a smaller number of variables have to be optimized, with the subsequent reduction in "development hassle"; and, (2) shorter development times, thanks to the fact that the adjustment of the weights (the artificial synapses) is a non-iterative, one-pass process. A backpropagation artificial neural network (BPANN), a second-order polynomial, and some less frequently employed polynomial and exponential functions (e.g., Gaussian, Lorentzian, and Boltzmann), were also evaluated for comparison purposes. The quality of the fit of the various models, assessed by calculating the root mean square of the percentage deviations, was as follows: GRANN>Boltzmann>second-order polynomial>BPANN>Gauss>Lorentz. The accuracy and precision of the models were further estimated through the determination of cadmium in the certified reference material "Trace Metals in Drinking Water" (High Purity Standards, Lot No. 490915), which has a cadmium certified concentration (12.00+/-0.06 microg L-1) that lies in the nonlinear regime of the calibration curve. Only the models generated by the GRANN and BPANN accurately predicted the concentrations of a series of solutions, prepared by serial dilution of the CRM, with cadmium concentrations below and above the maximum linear calibration limit (4.0 microg L-1). Extension of the working range by using the proposed methodology represents an attractive alternative from the analytical point of view, since it results in less specimen manipulation and consequently reduced contamination risks without compromising either the accuracy or the precision of the analyses. The implementation of artificial neural networks also helps to reduce the trial-and-error task of looking for the right mathematical model from among the many possibilities currently available in the various scientific and statistic software packages.

Journal Article↗