Neural Networks Convergence Using Physicochemical Data

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks have widely been exploited for those purposes. Although it is possible to model nonlinear behavior of short time series by using ANNs, very often they are not able to handle all events equally well. Therefore, alternative approaches have to be applied. In this study, a new, concurrent, performance-based methodology that combines best ANN topologies in order to decrease the forecasting errors and increase the forecasting certainty is proposed. The proposed approach is verified on three different data sets: the Serbian Gross National Income time series, the municipal traffic flow for a particular observation point, and the daily electric load consumption time series. It is shown that the method can significantly increase the forecasting accuracy of the individual networks, regardless of their topologies, which makes the methodology more applicable. For quantitative comparison of the accuracy of the proposed methodology with that of similar methodologies, a series of additional forecasting experiments that include a state-of-the-art ARIMA modelling and a combination of ANN and linear regression forecasting have been conducted.

Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks

This study examines the use of artificial neural networks as predictive tools for the growth of the dinoflagellate microalga Protoceratium reticulatum. Feed-forward back-propagation neural networks (FBN), using Levenberg-Marquardt back-propagation or Bayesian regularization as training functions, offered the best results in terms of representing the nonlinear interactions among all nutrients in a culture medium containing 26 different components. A FBN configuration of 26-14-1 layers was selected. The FBN model was trained using more than 500 culture experiments on a shake flask scale. Garson's algorithm provided a valuable means of evaluating the relative importance of nutrients in terms of microalgal growth. Microelements and vitamins had a significant importance (approximately 70%) in relation to macronutrients (nearly 25%), despite their concentrations in the culture medium being various orders of magnitude smaller. The approach presented here may be useful for modelling multi-nutrient interactions in photobioreactors.

Evaluation of new thiadiazoles and benzothiazoles as potential radioprotectors: free radical scavenging activity in vitro and theoretical studies (QSAR, DFT)

Thiol and aminothiol compounds are among the most efficient chemical radioprotectors. To increase their efficiency, we synthesized two new classes of thiol and aminothiol compounds derived from benzothiazole (T1, T2, AM1, AM2) and thiadiazole (T3, T4, AM3) structures. We examined them for their ability to scavenge free radicals (DPPH*, ABTS(*+), *OH). Thiol derivatives with a thiadiazole structure are the most active compounds scavenging DPPH* and ABTS(*+) free radicals, with an IC(50) of 0.053+/-0.006 and 0.023+/-0.002 mM, respectively, for the derivative T3. Moreover, compounds T1, T2, and T3 at 60 microM gave 83% protection against 2-deoxyribose degradation by *OH. The ability of these compounds to protect DNA against *OH produced by a Fenton reaction and gamma-irradiation (15 Gy)-induced strand breaks was also evaluated on pBR322 plasmid DNA. In both tests thiol derivatives were the most efficient compounds. Derivatives T2 and T3 totally inhibit DNA strand breaks at the concentration of 50 microM. The protection afforded by these derivatives was comparatively higher than that of the radioprotectors WR-2721 and WR-1065. Our data indicate that these two compounds are free radical scavengers and potential antioxidant agents. Finally, DFT and QSAR studies were performed to support the experimental observations.

Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network

This article attempts to increase the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins through improved learning. Most methods developed for improving the backpropagation algorithm of artificial neural networks are limited to small neural networks. Here, we introduce a guided-learning method suitable for networks of any size. The method employs a part of the weights for guiding and the other part for training and optimization. We demonstrate this technique by predicting residue solvent accessibility and real-value backbone torsion angles of proteins. In this application, the guiding factor is designed to satisfy the intuitive condition that for most residues, the contribution of a residue to the structural properties of another residue is smaller for greater separation in the protein-sequence distance between the two residues. We show that the guided-learning method makes a 2-4% reduction in 10-fold cross-validated mean absolute errors (MAE) for predicting residue solvent accessibility and backbone torsion angles, regardless of the size of database, the number of hidden layers and the size of input windows. This together with introduction of two-layer neural network with a bipolar activation function leads to a new method that has a MAE of 0.11 for residue solvent accessibility, 36 degrees for psi, and 22 degrees for phi. The method is available as a Real-SPINE 3.0 server in http://sparks.informatics.iupui.edu.

Novel computational models for predicting dopamine interactions

Dopamine is a crucial neurotransmitter responsible for functioning and maintenance of the nervous system. Dopamine has also been implicated in a number of diseases including schizophrenia, Parkinson's disease and drug addiction. Dopamine agonists are used in early Parkinson's disease treatment. Dopamine antagonists suppress schizophrenia. Therefore, molecules modulating dopamine receptors activity are vastly important for understanding the nervous system functioning and for the treatment of neurological diseases. In this study we describe novel computational models that efficiently predict binding affinity of the existing small molecule dopamine analogs to dopamine receptor. The model provides the set of molecular descriptors that can be used for the development of new small molecule dopamine agonists.