A kinetic spectrophotometric method for simultaneous determination of glycine and lysine by artificial neural networks

A spectrophotometric method for simultaneous analysis of glycine and lysine is proposed by application of neural networks on the spectral kinetic data. The method is based on the reaction of glycine and lysine with 1,2-naphthoquinone-4-sulfonate (NQS) in slightly basic medium. On the basis of the difference in the rate between the two reactions, these two amino acids can be determined simultaneously in binary mixtures. Feed-forward neural networks have been trained to quantify considered amino acids in mixtures under optimum conditions. In this way, a one-layer network was trained. Sigmoidal and linear transfer functions were used in the hidden and output layers, respectively. Linear calibration graphs were obtained in the concentration range of 1 to 25microgml(-1) for glycine and 1 to 19microgml(-1) for lysine. The analytical performance of this method was characterized by the relative standard error. The proposed method was applied to the determination of considered amino acids in synthetic samples.     

Implementing Gaussian process inference with neural networks

Gaussian processes compare favourably with backpropagation neural networks as a tool for regression, and Bayesian neural networks have Gaussian process behaviour when the number of hidden neurons tends to infinity. We describe a simple recurrent neural network with connection weights trained by one-shot Hebbian learning. This network amounts to a dynamical system which relaxes to a stable state in which it generates predictions identical to those of Gaussian process regression. In effect an infinite number of hidden units in a feed-forward architecture can be replaced by a merely finite number, together with recurrent connections.     

Chemiluminescent multiassay of pesticides with horseradish peroxidase as a label

Competitive chemiluminescent immunoassay based on a combination of five antibodies was used in a combination with neural network to identify and estimate amounts of three cross-reacting s-triazines (atrazine, terbythylazine and ametryn). Antibodies with different cross-reactivity towards s-triazines were immobilized in separate wells an eight-well microtiter strip. Training of neural networks was carried out with four different learning procedures. The best topology for the data measured was a net with two hidden layers with ten neurons in the first and 15 in the second layer trained with the Schmidhuber method. s-Triazine classification of environmental samples containing various analyte mixtures was correct in 70-100% of all cases depending on the type of analyte. The test developed can be proposed as an alternative field test for multianalyte environmental monitoring.     

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network

B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.     

Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions

In order to better manage the limited water resources in arid regions, accurate determination of plant water requirements is necessary. For that, the evaluation of reference evapotranspiration (ET0)--a basic component of the hydrological cycle--is essential. In this context, the Penman Monteith equation, known for its accuracy, requires a high number of climatic parameters that are not always fully available from most meteorological stations. Our study examines the effectiveness of the use of artificial neural networks (ANN) for the evaluation of ET0 using incomplete meteorological parameters. These neural networks use daily climatic data (temperature, relative humidity, wind speed and the insolation duration) as inputs, and ET0 values estimated by the Penman-Monteith formula as outputs. The results show that the proper choice of neural network architecture allows not only error minimization but also maximizes the relationship between the dependent variable and the independent variables. In fact, with a network of two hidden layers and eight neurons per layer, we obtained, during the test phase, values of 1, 1 and 0.01 for the determination coefficient, the criterion of Nash and the mean square error, respectively. Comparing results between multiple linear regression and the neural method revealed the good modeling quality and high performance of the latter, due to the possibility of improving performance criteria. In this work, we considered correlations between input variables that improve the accuracy of the model and do not pose problems of multi-collinearity. Furthermore, we succeeded in avoiding overfitting and could generalize the model for other similar areas.     

Use of neural networks in the mathematical modelling of the enzymic synthesis of amoxicillin catalysed by penicillin G acylase immobilized in chitosan

This study focuses in the mathematical modelling of the enzymic synthesis of amoxicillin by the reaction of p-hydroxyphenylglycine methyl ester and 6-aminopenicillanic acid (6APA), catalyzed by penicillin G acylase (PGA) immobilized on glutaraldehyde-chitosan, at 25°C and pH 6.5. Previous work on the kinetics and mechanism of reaction showed that the use of neural networks seems to be an interesting alternative to simulate experimental data of antibiotic production. Therefore, two feedforward neural networks, with one hidden layer, were trained and used to forecast the rates of amoxicillin and p-hydroxyphenylglycine (POHPG) net production. First of all, some parameters that affect the network performed were investigated, such as the number of nodes between the input and hidden layers and the number of interactions during the learning phase. Afterwards, hybrid models that coupled artificial neural networks to mass-balance equations were used to reproduce the performance of batch reactors for the production of amoxicillin. This approach provided accurate results, within the range of substrate concentration studied.     

Prediction of ATP/GTP-binding motif: a comparison of a perceptron type neural network and a consensus sequence method [corrected

Neural networks have been applied to a number of protein structure problems. In some applications their success has not been substantiated by a comparison with the performance of a suitable alternative statistical method on the same data. In this paper, a two-layer feed-forward neural network has been trained to recognize ATP/GTP-binding [corrected] local sequence motifs. The neural network correctly classified 78% of the 349 sequences used. This was much better than a simple motif-searching program. A more sophisticated statistical method was developed, however, which performed marginally better (80% correct classification) than the neural network. The neural network and the statistical method performed similarly on sequences of varying degrees of homology. These results do not imply that neural networks, especially those with hidden layers, are not useful tools, but they do suggest that two-layer networks in particular should be carefully tested against other statistical methods.     

Application of neural networks to the classification of pancreatic intraductal proliferative lesions.

The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back-propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.     

Prediction of ubiquitin proteins using artificial neural networks, hidden markov model and support vector machines

Ubiquitin functions to regulate protein turnover in a cell by closely regulating the degradation of specific proteins. Such a regulatory role is very important, and thus I have analyzed the proteins that are ubiquitin-like, using an artificial neural network, support vector machines and a hidden Markov model (HMM). The methods were trained and tested on a set of 373 ubiquitin proteins and 373 non-ubiquitin proteins, obtained from Entrez protein database. The artificial neural network and support vector machine are trained and tested using both the physicochemical properties and PSSM matrices generated from PSI-BLAST, while in the HMM based method direct sequences are used for training-testing procedures. Further, the performance measures of the methods are calculated for test sequences, i.e. accuracy, specificity, sensitivity and Matthew's correlation coefficients of the methods are calculated. The highest accuracy of 90.2%, specificity of 87.04% and sensitivity of 94.08% was achieved using the support vector machine model with PSSM matrices. While accuracies of 86.82%, 83.37%, 80.18% and 72.11% were obtained for the support vector machine with physicochemical properties, neural network with PSSM matrices, neural networks with physicochemical properties, and hidden Markov model, respectively. As the accuracy for SVM model is better both using physicochemical properties and the PSSM matrices, it is concluded that kernel methods such as SVM outperforms neural networks and hidden Markov models.     

A neural network architecture for data classification

This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.     

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.     

Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks

A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains. Training was performed using 4136 cysteine-containing segments extracted from 969 non-homologous proteins of well-resolved 3D structure and without chain-breaks. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 80% using neural networks based on evolutionary information. When the whole protein is taken into account by means of an HMM, a hybrid system is generated, whose emission probabilities are computed using the NN output (hidden neural networks). In this case, the predictor accuracy increases up to 88%. Further, when tested on a protein basis, the hybrid system can correctly predict 84% of the chains in the data set, with a gain of at least 27% over the NN predictor.     

Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks

In order to process data of proteins, a numerical representation for an amino acid is often necessary. Many suitable parameters can be derived from experiments or statistical analysis of databases. To ensure a fast and efficient use of these sources of information, a reduction and extraction of relevant information out of these parameters is a basic need. In this approach established methods like principal component analysis (PCA) are supplemented by a method based on symmetric neural networks. Two different parameter representations of amino acids are reduced from five and seven dimensions, respectively, to one, two, three, or four dimensions by using a symmetric neural network approach alternatively with one or three hidden layers. It is possible to create general reduced parameter representations for amino acids. To demonstrate the ability of this approach, these reduced sets of parameters are applied for the ab initio prediction of protein secondary structure from primary structure only. Artificial neural networks are implemented and trained with a diverse representation of 430 proteins out of the PDB. An essentially faster training and also prediction without a decrease in accuracy is obtained for the reduced parameter representations in comparison with the complete set of parameters. The method is transferable to other amino acids or even other molecular building blocks, like nucleic acids, and therefore represents a general approach.Electronic Supplementary Material available.     

Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

This study represents an ANN based computational scheming of physical, chemical and biological parameters at flask level for mass multiplication of plants through micropropagation using bioreactors of larger volumes. The optimal culture environment at small scale for Glycyrrhiza plant was predicted by using neural network approach in terms of pH and volume of growth medium per culture flask, incubation room temperature and month of inoculation along with inoculum properties in terms of inoculum size, fresh weight and number of explant per flask. This kind of study could be a model system in commercial propagation of various economically important plants in bioreactors using tissue culture technique. In present course of study the ANN was trained by implementing MATLAB neural network. A feed-forward back propagation type network was created for input vector (seven input elements), with single hidden layer (seven nodes) and one output unit in output layer. The ‘tansig’ and ‘purelin’ transfer functions were adapted for hidden and output layers respectively. The four training functions viz. traingda, trainrp, traincgf, traincgb were randomly selected to train four networks which further examined with available dataset. The efficiency of neural networks was concluded by the comparison of results obtained from this study with that of empirical data obtained from the detailed tissue culture experiments and designated as Target set (mean fresh weight biomass per culture flask after 40 days of in vitro culture duration). Efficiency of networks for better training initialization was judged on the basis of comparative analysis of ‘Mean Square Error at zero epoch’ for each network trained in which the least error at initial point was observed with trainrp followed by traincgb and traincgf. A comparative assessment between experimental target data range obtained from wet lab practice and all trained network output range for the efficiency of trained networks for least deviation from target range revealed the output range of network ‘trainrp’ was closest to the empirical target range while least comparison was worked out from network ‘traincgb’ which had output range more than the target decided and ultimately showed meaningless result.     

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.     

Functional model of biological neural networks

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.     

The prediction of membrane protein structure and genome structural annotation

New methods, essentially based on hidden Markov models (HMM) and neural networks (NN), can predict the topography of both beta-barrel and all-alpha membrane proteins with high accuracy and a low rate of false positives and false negatives. These methods have been integrated in a suite of programs to filter proteomes of Gram-negative bacteria, searching for new membrane proteins.     

Feedforward neural network with adaptive reference pattern layer.

A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid can provide significant advantages over standard MLPs and RBFs in terms of fast and efficient learning, and compact network structure.     

Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models

A simple and sensitive spectrophotometric method to resolve ternary mixtures of tryptophan (Trp), tyrosine (Tyr), and histidine (His) in synthetic and water samples is described. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K(3)Fe(CN)(6)) in alkaline medium. The absorbance data were monitored on the analytical wavelength (420 nm) of K(3)Fe(CN)(6) spectrum. Synthetic mixtures of the three amino acids were analyzed, and the data obtained were processed by principal component-artificial neural network (PC-ANN) models. After reducing the number of spectral data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Tangent and sigmoidal transfer function were used in the hidden and output layers, respectively. The analytical performance of this method was characterized by relative standard error. The method allowed the determination of Trp, Tyr, and His at concentrations between 10 and 55, 10 and 60, and 10 and 40 microg ml(-1), respectively. The results show that the PC-ANN is an efficient method for prediction of the three analytes.     

Adaptive Neural Network Structure Optimization Algorithm Based on Dynamic Nodes

Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time. Moreover, existing neural network topology optimization algorithms have the disadvantage of many calculations and complex network structure modeling. We propose a Dynamic Node-based neural network Structure optimization algorithm (DNS) to handle these issues. DNS consists of two steps: the generation step and the pruning step. In the generation step, the network generates hidden layers layer by layer until accuracy reaches the threshold. Then, the network uses a pruning algorithm based on Hebb’s rule or Pearson’s correlation for adaptation in the pruning step. In addition, we combine genetic algorithm to optimize DNS (GA-DNS). Experimental results show that compared with traditional neural network topology optimization algorithms, GA-DNS can generate neural networks with higher construction efficiency, lower structure complexity, and higher classification accuracy.