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PubMed1991

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.

Adenosine Triphosphate↗
PubMed1997

Probabilistic neural network prediction of ascites in broilers based on minimally invasive physiological factors.

A Probabilistic Neural Network (PNN) was trained to predict ascites in broilers based on minimally invasive inputs (i.e., physiological factors that do not require the death of the bird). A PNN is a supervised, three-layer, artificial neural network that classifies input patterns (e.g., physiological data) into specific output categories (e.g., ascites or no ascites). The PNN inputs were O2 level in the blood, body weight, electrocardiogram (ECG), hematocrit, S wave, and heart rate of individual birds. These data were from three experiments that have been described previously (Roush et al., 1996a,b). The three data sets were pooled into a combined data set for a total of 170 observations. From the pooled data, a training set (117 birds), a calibration set (17 birds), and a verification set (36 birds) were extracted. The PNN was trained on the training data set. To prevent the PNN from overfitting the training data, the neural network was evaluated on its ability to make correct predictions of the calibration data set. At the point at which the neural network made the highest number of correct classifications for the calibration data set, the trained neural network was saved on the computer. When the PNN was applied to the complete data set, the sensitivity or proportion of the birds with ascites that the PNN correctly diagnosed was 0.97 (75/77 birds). The specificity or proportion of birds that the PNN made a correct diagnosis of not having ascites was 0.98 (91/93 birds). When the PNN was applied to the verification data set, which was not subjected to neural network training, the sensitivity was 0.95 (19/20) and the specificity was 0.88 (14/16 birds). Use of models developed with artificial neural networks may enhance the diagnosis of ascites in broilers. The results may be useful in choosing and developing broiler strains that do not have a propensity for ascites.

Animals↗
PubMed2007

An interagency network perspective on HIV prevention.

OBJECTIVE: The objective of this study was to describe the network structures of agencies and individuals engaged in HIV prevention in a North Carolina county. STUDY DESIGN: The authors conducted a cross-sectional study of interconnected agencies and individuals identified through snowball sampling. Participants were interviewed face to face with a structured questionnaire about interactions with others in the network. Network characteristics such as density and centralization were computed with UCINET software. RESULTS: The network of 11 agencies had an information exchange density of 14%. Exchanges of clients and funds were even more rare. The network of 17 individuals influential in HIV prevention was better connected with a density of 42%. CONCLUSIONS: The HIV prevention agencies in the county were not functioning as a network and thus were not benefiting the community to their full potential. Any connectedness between the agencies may be attributable to the better connections between influential individuals, several of whom worked for the agencies in the agency network.

Community Networks↗
PubMed2000

History influences signal recognition: neural network models of túngara frogs.

Animals often attend to only a few of the cues provided by the complex displays of conspecifics. We suggest that these perceptual biases are influenced by mechanisms of signal recognition inherited from antecedent species. We tested this hypothesis by manipulating the evolutionary history of artificial neural networks, observing how the resulting networks respond to many novel stimuli and comparing these responses to the behaviour of females in phonotaxis experiments. Networks with different evolutionary histories proved equally capable of evolving to recognize the call of the túngara frog, Physalaemus pustulosus, but exhibited distinct responses to novel stimuli. History influenced the ability of networks to predict known responses of túngara frogs; network accuracy was determined by how closely the network history approximated the hypothesized history of the túngara frog. Our findings emphasize the influence of past selection pressures on current perceptual mechanisms, and demonstrate how neural network models can be used to address behavioural questions that are intractable through traditional methods.

Algorithms↗
PubMed2019

Replication timing networks reveal a link between transcription regulatory circuits and replication timing control.

DNA replication occurs in a defined temporal order known as the replication timing (RT) program and is regulated during development, coordinated with 3D genome organization and transcriptional activity. However, transcription and RT are not sufficiently coordinated to predict each other, suggesting an indirect relationship. Here, we exploit genome-wide RT profiles from 15 human cell types and intermediate differentiation stages derived from human embryonic stem cells to construct different types of RT regulatory networks. First, we constructed networks based on the coordinated RT changes during cell fate commitment to create highly complex RT networks composed of thousands of interactions that form specific functional subnetwork communities. We also constructed directional regulatory networks based on the order of RT changes within cell lineages, and identified master regulators of differentiation pathways. Finally, we explored relationships between RT networks and transcriptional regulatory networks (TRNs) by combining them into more complex circuitries of composite and bipartite networks. Results identified novel trans interactions linking transcription factors that are core to the regulatory circuitry of each cell type to RT changes occurring in those cell types. These core transcription factors were found to bind cooperatively to sites in the affected replication domains, providing provocative evidence that they constitute biologically significant directional interactions. Our findings suggest a regulatory link between the establishment of cell-type-specific TRNs and RT control during lineage specification.

Cell Differentiation↗
PubMed2004

Constraining the topology of neural networks to ensure dynamics with symmetry properties.

This paper addresses the training of network models from data produced by systems with symmetry properties. It is argued that although general networks are global approximators, in practice some properties such as symmetry are very hard to learn from data. In order to guarantee that the final network will be symmetrical, constraints are developed for two types of models, namely, the multilayer perceptron (MLP) network and the radial basis function (RBF) network. In global modeling problems it becomes crucial to impose conditions for symmetry in order to stand a chance of reproducing symmetry-related phenomena. Sufficient conditions are given for MLP and RBF networks to have a set of fixed points that are symmetrical with respect to the origin of the phase space. In the case of MLP networks such conditions reduce to the absence of bias parameters and the requirement of odd activation functions. This turns out to be important from a dynamical point of view since some phenomena are only observed in the context of symmetry, which is not a structurally stable property. The results are illustrated using bench systems that display symmetry, such as the Duffing-Ueda oscillator and the Lorenz system.

Algorithms↗
PubMed2002

Assortative mixing in networks.

A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.

Computer Communication Networks↗
PubMed2004

Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification.

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Algorithms↗
PubMed2005

A novel neural network for variational inequalities with linear and nonlinear constraints.

Variational inequality is a uniform approach for many important optimization and equilibrium problems. Based on the sufficient and necessary conditions of the solution, this paper presents a novel neural network model for solving variational inequalities with linear and nonlinear constraints. Three sufficient conditions are provided to ensure that the proposed network with an asymmetric mapping is stable in the sense of Lyapunov and converges to an exact solution of the original problem. Meanwhile, the proposed network with a gradient mapping is also proved to be stable in the sense of Lyapunov and to have a finite-time convergence under some mild condition by using a new energy function. Compared with the existing neural networks, the new model can be applied to solve some nonmonotone problems, has no adjustable parameter, and has lower complexity. Thus, the structure of the proposed network is very simple. Since the proposed network can be used to solve a broad class of optimization problems, it has great application potential. The validity and transient behavior of the proposed neural network are demonstrated by several numerical examples.

Algorithms↗
PubMed2006

Generalized RLS approach to the training of neural networks.

Recursive least square (RLS) is an efficient approach to neural network training. However, in the classical RLS algorithm, there is no explicit decay in the energy function. This will lead to an unsatisfactory generalization ability for the trained networks. In this paper, we propose a generalized RLS (GRLS) model which includes a general decay term in the energy function for the training of feedforward neural networks. In particular, four different weight decay functions, namely, the quadratic weight decay, the constant weight decay and the newly proposed multimodal and quartic weight decay are discussed. By using the GRLS approach, not only the generalization ability of the trained networks is significantly improved but more unnecessary weights are pruned to obtain a compact network. Furthermore, the computational complexity of the GRLS remains the same as that of the standard RLS algorithm. The advantages and tradeoffs of using different decay functions are analyzed and then demonstrated with examples. Simulation results show that our approach is able to meet the design goals: improving the generalization ability of the trained network while getting a compact network.

Algorithms↗
PubMed2006

Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm.

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the HTGA approach can be more robust, statistically sound, and quickly convergent. First, the authors evaluate the performance of the presented HTGA approach by studying some global numerical optimization problems. Then, the presented HTGA approach is effectively applied to solve three examples on forecasting the sunspot numbers, tuning the associative memory, and solving the XOR problem. The numbers of hidden nodes and the links of the feedforward neural network are chosen by increasing them from small numbers until the learning performance is good enough. As a result, a partially connected feedforward neural network can be obtained after tuning. This implies that the cost of implementation of the neural network can be reduced. In these studied problems of tuning both network structure and parameters of a feedforward neural network, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the presented HTGA approach can obtain better results than the existing method reported recently in the literature.

Algorithms↗
PubMed2006

Dynamics analysis and analog associative memory of networks with LT neurons.

The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors.

Algorithms↗
PubMed2006

Solving pseudomonotone variational inequalities and pseudoconvex optimization problems using the projection neural network.

In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network.

Algorithms↗
PubMed2006

Evaluation of neural network robust reliability using information-gap theory.

A novel technique for the evaluation of neural network robustness against uncertainty using a nonprobabilistic approach is presented. Conventional optimization techniques were employed to train multilayer perceptron (MLP) networks, which were then probed with an uncertainty analysis using an information-gap model to quantify the network response to uncertainty in the input data. It is demonstrated that the best performing network on data with low uncertainty is not in general the optimal network on data with a higher degree of input uncertainty. Using the concepts of information-gap theory, this paper develops a theoretical framework for information-gap uncertainty applied to neural networks, and explores the practical application of the procedure to three sample cases. The first consists of a simple two-dimensional (2-D) classification network operating on a known Gaussian distribution, the second a nine-lass vibration classification problem from an aircraft wing, and the third a two-class example from a database of breast cancer incidence.

Algorithms↗
PubMed2006

Neural networks for continuous online learning and control.

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Algorithms↗
PubMed2003

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

Amino Acid Sequence↗
PubMed1999

Sonographic prediction of malignancy in adnexal masses using an artificial neural network.

OBJECTIVE: To generate a neural network algorithm which computes a probability of malignancy score for pre-operative discrimination between malignant and benign adnexal tumours. DESIGN: A retrospective analysis of previously collected data. Information from 75% of the study group was used to train an artificial neural network and the remainder was used for validation. SETTING: The Gynaecological Ultrasound Research Unit at King's College Hospital, London. POPULATION: Sixty-seven women with known adnexal mass who had been examined using transvaginal B-mode ultrasonography and colour Doppler imaging with pulse spectral analysis immediately before surgery. The excised masses were classified histologically as benign (n = 52) or malignant (n = 15), of which three were borderline. METHODS: The variables that were put into the artificial neural network were: age, menopausal status, maximum tumour diameter, tumour volume, locularity, the presence of papillary projections, the presence of random echogenicity, the presence of analysable blood flow velocity waveforms, the peak systolic velocity, time-averaged maximum velocity, the pulsatility index, and resistance index. Histological classification, categorised as benign or malignant, was the output result. RESULTS: A variant of the back propagation method was selected to train the network. The overall architecture of the network with the best performance contained an input layer with four variables (age, time-averaged maximum velocity, papillary projection score and maximum tumour diameter), a hidden layer with three units and an output layer with one. The sensitivity and specificity at the optimum diagnostic decision value for the artificial neural network output (0.45) were 100% (95% CI 78.2%-100%) and 98.1% (95% CI 89.5%-100%), respectively. These values were significantly better than those obtained from the independent use of the resistance index, pulsatility index, time-averaged maximum velocity or peak systolic velocity at their optimum decision values (P < 0.01). CONCLUSION: Artificial neural networks may be used on clinical and ultrasound derived end-points to accurately predict ovarian malignancy. There is a need for a prospective evaluation of this technique using a larger number of patients.

Adnexal Diseases↗
PubMed2006

Patient gender and radiopharmaceutical tracer is of minor importance for the interpretation of myocardial perfusion images using an artificial neural network.

The purpose of this study was to assess the influence of patient gender and choice of perfusion tracer on computer-based interpretation of myocardial perfusion images. For the image interpretation, an automated method was used based on image processing and artificial neural network techniques. A total of 1000 patients were studied, all referred to the Royal Brompton Hospital in London for myocardial perfusion scintigraphy over a period of 1 year. The patients were randomized to receive either thallium or one of the two technetium tracers, methoxyisobutylisonitrile or tetrofosmin. Artificial neural networks were trained with either mixed gender or gender-specific and mixed tracer or tracer-specific training sets of different sizes. The performance of the networks was assessed in separate test sets, with the interpretation of experienced physicians regarding the presence or absence of fixed or reversible defects in the images as the gold standard. The neural networks trained with large mixed gender training sets were as good as the networks trained with gender-specific data sets. In addition, the neural networks trained with large mixed tracer training sets were as good as or better than the networks trained with tracer-specific data sets. Our results indicate that the influence of patient gender and perfusion tracer are of minor importance for the computer-based interpretation of the myocardial perfusion images. The differences that occur can be compensated for by larger training sets.

Adult↗