Application of the volume learning algorithm artificial neural networks for recognition of the type of interaction between neurons from their cross-correlation histograms

An algorithm based on two types artificial neural networks (ANNs) is proposed. The first network is an associative ANN while the second network is a Self-Organizing Map of Kohonen. The results for a test set are similar to the performance of our pre-vious expert system algorithm developed with Group Method of Data Handling (GMDH). However, while GMDH uses indices derived using the expert knowledge (and thus require considerable time and resources) the VLA process initial raw data.Для решения задачи распознавания типов взаимодействия между нейронами предложен алгоритм, основанный на использовании двух типов искусственных нейронных сетей (ИНС). Первая сеть представляет собой ассоциативную ИНС, тогда как вторая — самоорганизующиеся карты Кохонена. Результаты, полученные для тестового набора данных, подобны результатам, найденным методом группового учета аргументов (МГУА). Однако новый подход использует только исходные данные, тогда как МГУА — производные индексов, полученные дополнительным анализом начальных индексов.Для вирішення задачі розпізнавання типів взаємодії між нейронами запропоновано алгоритм, заснований на використанні двох типів штучних нейронних мереж (ШНМ). Перша мережа представляє собою асоціативну ШНМ, тоді як друга — карту Кохонена, що самоорганізується. Результати тестування на наборі даних подібні до результатів, отриманих методом групового врахування аргументів (МГВА). Однак новий підхід використовує тільки початкові дані, тоді як МГВА — похідні індексів, отримані додатковим аналізом початкових індексів

Spike separation based on symmetries analysis in phase space

The present study introduces an approach for automatic classification of extracellularly recorded action potentials of neurons based on geometrical approach. Neuronal spikes are considered as geometrical objects, namely trajectories in phase space. It is shown that for spikes, generated by the same neuron, it is possible to find such a symmetry transformation under which their trajectories are invariant in phase space. On the other hand, the phase trajectories of spikes generated by other neurons change significantly under the action of that transformation. Thus, it is possible to define a special symmetry transformation that only typifies the spikes of the given neuron. The proposed algorithm is explained and an overview of the mathematical background is given. The method was tested on simulated data and showed good results in real experiments.Запропоновано підхід до автоматичної класифікації внутрішньокліткових потенціалів нейронів, заснований на геометричному підході. Нейронні спайки (викиди) розглядаються як геометричні об’єкти, а саме як траєкторії у фазовому просторі. Показано, що для спайків, згенерованих одним нейроном, можна знайти такі перетворення симетрії, под дією яких ці треєкторії інваріантні у фазовому просторі. З іншого боку, фазові траєкторії спайків, сгенерованих іншими нейронами, змінюються значною мырою під дією перетворень. Таким чином, можна ввести спеціальні перетворення сіметрії, які відповідають конкретному нейрону. Описано запропонований алгоритм та наведено огляд математичних основ методу. Метод тестовано за спеціальними даними, отримані позитивні результати

Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection

The estimation of the accuracy of predictions is a critical problem in QSAR modeling. The "distance to model" can be defined as a metric that defines the similarity between the training set molecules and the test set compound for the given property in the context of a specific model. It could be expressed in many different ways, e.g., using Tanimoto coefficient, leverage, correlation in space of models, etc. In this paper we have used mixtures of Gaussian distributions as well as statistical tests to evaluate six types of distances to models with respect to their ability to discriminate compounds with small and large prediction errors. The analysis was performed for twelve QSAR models of aqueous toxicity against T. pyriformis obtained with different machine-learning methods and various types of descriptors. The distances to model based on standard deviation of predicted toxicity calculated from the ensemble of models afforded the best results. This distance also successfully discriminated molecules with low and large prediction errors for a mechanism-based model developed using log P and the Maximum Acceptor Superdelocalizability descriptors. Thus, the distance to model metric could also be used to augment mechanistic QSAR models by estimating their prediction errors. Moreover, the accuracy of prediction is mainly determined by the training set data distribution in the chemistry and activity spaces but not by QSAR approaches used to develop the models. We have shown that incorrect validation of a model may result in the wrong estimation of its performance and suggested how this problem could be circumvented. The toxicity of 3182 and 48774 molecules from the EPA High Production Volume (HPV) Challenge Program and EINECS (European chemical Substances Information System), respectively, was predicted, and the accuracy of prediction was estimated. The developed models are available online at http://www.qspr.org site

The perspectives of computational chemistry modeling.

The on-line tools for computational chemistry modeling will be increasingly used in the future. This will bring the advantages both for the authors and the readers

Internet in Drug Design and Discovery.

The development of Internet technologies and the WWW has dramatically influenced all aspects of modern life. Is their development also beneficial for drug discovery? This article reviews advantages of web technologies and thier influence on the discovery process

Robustness in experimental design: A study on the reliability of selection approaches.

The quality criteria for experimental design approaches in chemoinformatics are numerous. Not only the error performance of a model resulting from the selected compounds is of importance, but also reliability, consistency, stability and robustness against small variations in the dataset or structurally diverse compounds. We developed a new stepwise, adaptive approach, DescRep, combining an iteratively refined descriptor selection with a sampling based on the putatively most representative compounds. A comparison of the proposed strategy was based on statistical performance of models derived from such a selection to those derived by other popular and frequently used approaches, such as the Kennard-Stone algorithm or the most descriptive compound selection. We used three datasets to carry out a statistical evaluation of the performance, reliability and robustness of the resulting models. Our results indicate that stepwise and adaptive approaches have a better adaptability to changes within a dataset and that this adaptability results in a better error performance and stability of the resulting models

Cross-frequency coupling in mesiotemporal EEG recordings of epileptic patients.

Semi-invasive foramen ovale (Fov) electrodes were used to record electrical activity in the vicinity of the inferior mesial temporal region of epileptic patients, in addition to standard scalp EEG. Third order cumulant analysis was used to measure the phase-coupled frequencies corresponding to non-linear coupling of spectral frequency components, somewhat analogous to frequencies of resonance. On the basis of the distribution of these frequencies, an index of resonance (IR) is defined as the ratio between the number of peaks in the gamma-band (40-55Hz) vs. the number of peaks in the beta-band (15-30Hz). The epileptogenic focus was located in the hemisphere with lower resonant frequencies because these frequencies were characteristic of a spread of the seizure over a broader area. In the case of Fov electrodes IR could differentiate a group of patients affected by a tumor compared to patients with mesial temporal sclerosis. The novel index IR appears as an interesting parameter to evaluate the level of interareal functional connectivity in Fov recordings in epileptic patients, but its usage is likely to be extended in electrophysiological studies

From Big Data to Artificial Intelligence: Chemoinformatics meets new challenges.

Abstract: The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by “Big Data in Chemistry” project and draws perspectives on the future progress of the field. Graphical Abstract: [Figure not available: see fulltext.]