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Biomedical subjects

I V Tetko

Publications and source records attributed to I V Tetko.

14 recordsLinked to original sources
PubMed1999

Pharmaceutical fingerprinting in phase space. 1. Construction of phase fingerprints.

The present study proposes a general method for constructing pharmaceutical fingerprints in the analysis of HPLC trace organic impurity patterns. The approach considers signals in phase space and accounts for two different types of noise: additive and perturbative. The first type, additive noise, contributes to distortion of the absolute values of signal peaks. The second type, perturbative noise, contributes to variations of the retention times of signal peaks and distorts the time scale of the trace organic impurity patterns. The ability of the proposed approach to consider both types of noise significantly distinguishes it from existing methods of data analysis that are usually designed to treat only the additive noise. Analysis of the HPLC signals in phase space eliminates the problem of perturbation noise and enables detection and comparison of similar signal segments recorded at different retention times. The current study analyzes the chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan using three HPLC columns. For five manufacturers the variability of data recorded with the same column are in perfect agreement with the proposed model. A significant variance of parameters is detected for one manufacturer, thus indicating a possible change in its product consistency. The analysis in phase space is also used to explain the previously detected variability of HPLC signals across columns. The accompanying paper reports an application of the proposed approach for the pattern recognition of HPLC data.

Algorithms↗
PubMed1999

Pharmaceutical fingerprinting in phase space. 2. Pattern recognition.

The current study introduces an approach for pattern recognition of drug manufacturers according to their HPLC trace impurity data. This method considers signals in phase space and accounts for two different types of noise: additive and perturbative. The pharmaceutical fingerprints are estimated as mean trajectories of HPLC trace impurity data and are used as reference models for recognition of new data by the minimal length classifier. The chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan are analyzed as an example. The prediction ability of the new method tested using three different cross-validation procedures remains about 95% even if the number of available data in the training sets decreases by 5 times. The accuracy of prediction in phase space is superior compared to results calculated using a Window Preprocessing method and artificial neural networks. The difference in performance between new and previous methods becomes more significant under particular conditions that are more adequate for practical application of the method. In addition, the current approach enables simple and comprehensive interpretation of the calculated results.

Artificial Intelligence↗
PubMed1999

Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task.

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.

Acoustic Stimulation↗
PubMed1999

Corticofugal modulation of functional connectivity within the auditory thalamus of rat, guinea pig and cat revealed by cooling deactivation.

Microelectrode recordings were simultaneously performed at multiple sites in the medial geniculate body (MGB) of anesthetized cats, rats and guinea pigs. We studied the effect of cortical deactivation on the association of neural activity within the thalamus during spontaneous activity. The corticofugal influence was suppressed by temporary cooling of the auditory cortex. Pairs of spike trains recorded from the same electrode were distinguished from cases where units were in MGB but recorded with different electrodes. Time domain analyses included crosscorrelations and search for precise repetition of complex spatiotemporal firing patterns of reverberating thalamic circuits. As a complementary approach we performed bispectral analyses of simultaneously recorded local field potentials in order to uncover the frequency components of their power spectra which are non linearly coupled. All results suggest that new functional neuronal circuits might appear at the thalamic level in the absence of input from the cortex. The newly active intrathalamic connections would provide the necessary input to sustain the reverberating activity of thalamic cell assemblies and generate low frequency non-linear interactions. The dynamic control exerted by the cortex over the functional segregation of information processing carried out in the thalamus conforms with theoretical neural network studies and with the functional selectivity-adaptive filtering theory of thalamic neuronal assemblies. Although this general conclusion remains valid across species, specific differences are discussed in the frame of known differences of the microcircuitry elements.

Animals↗
PubMed1999

Prediction of partition coefficient based on atom-type electrotopological state indices.

The aim of this study was to determine the efficacy of atom-type electrotopological state indices for estimation of the octanol-water partition coefficient (log P) values in a set of 345 drug compounds or related complex chemical structures. Multilinear regression analysis and artificial neural networks were used to construct models based on molecular weights and atom-type electrotopological state indices. Both multilinear regression and artificial neural networks provide reliable log P estimations. For the same set of parameters, application of neural networks provided better prediction ability for training and test sets. The present study indicates that atom-type electrotopological state indices offer valuable parameters for fast evaluation of octanol-water partition coefficients that can be applied to screen large databases of chemical compounds, such as combinatorial libraries.

Chemical Phenomena↗
PubMed1998

Computer assisted neurophysiology by a distributed Java program.

We have developed a distributed software package which allows neurophysiologists to have at hand a set of network and graphic tools in order to perform the analysis of data collected from the electrical activity of neurons, studied in the experimental laboratory. This system is built around three major components. First, the experimental data are processed in order to select specific data files from the networked computer and display the spike trains as raster dots. The second part selects one type of analysis (e.g., cross-correlation, out of a set of possible choices) after configuration of some specific parameters. Finally, the third component deals with the results of data analyses, which are selected and displayed on a multi-parameter graphic "ring binder." We have used the Java programming language to implement this application. This recent object-oriented language offers platform-independent software tools including graphic package and standard network protocols.

Algorithms↗
PubMed1997

Data modelling with neural networks: advantages and limitations.

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.

Data Interpretation, Statistical↗
PubMed1995

[Evolutionary programming for finding "structure-activity" relationships in a series of 3-phenoxychromone and 3-phenoxy-4-hydroxycoumarin derivatives].

Based on a set of compounds possessing hypolipidemic activity, it was demonstrated that evolutionary algorithms can be successfully used to compile an informative set of molecular parameters. The parameter sets selected using the method of potential functions allowed correct prediction of the activity of test molecules.

4-Hydroxycoumarins↗
PubMed1994

HIV-1 reverse transcriptase inhibitor design using artificial neural networks.

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.

Algorithms↗
PubMed1993

Applications of neural networks in structure-activity relationships of a small number of molecules.

We investigated the applications of back propagation artificial neural networks (ANN) for a small dataset analysis in the field of structure-activity relationships. The derivatives of carboquinone were used as an example. It's been found that in this case the use of the same neural network results in unambiguous classification of new molecules. Predictions can be improved with statistical analysis of independent prognosis sets. We suggest that the sign criterion be used as a classification rule. We also compared neural networks with FALS and ALS in leave-one-out prediction. ANN applied to the same dataset has shown the same predictive ability as ALS but poorer than FALS.

Carbazilquinone↗
PubMedDate not supplied

Dynamical cell assemblies in the rat auditory cortex in a reaction-time task.

Simultaneous single unit spike trains were recorded in the auditory cortex of freely moving rats performing a complex cognitive task. The experimental paradigm is based on a two-choice task (Go/Nogo) with a two-component (pitch and location) auditory stimulus lasting 500 ms. We report evidence that firstly functional interactions, measured by cross-correlation analysis, between single units in the auditory cortex are dynamically modified in the period preceding the onset of the auditory stimulation, referred to as the 'waiting period'. We secondly observed that spatio-temporal firing patterns both within, and across cell spike trains also tended to appear in the waiting period, several seconds before the actual stimulus delivery. These patterns indicate a very precise repetition of spike discharges separated by long intervals (up to several hundreds of milliseconds). No consistent changes in mean rate were observed. These results suggest that network activity in the auditory cortex is selectively modified in rate independent ways before the actual sensory stimulation. These modifications may reflect participation of recurrent neuronal networks in processes anticipating the expected sensory input.

Action Potentials↗
PubMedDate not supplied

Neural network studies. 2. Variable selection.

Quantitative structure-activity relationship (QSAR) studies usually require an estimation of the relevance of a very large set of initial variables. Determination of the most important variables allows theoretically a better generalization by all pattern recognition methods. This study introduces and investigates five pruning algorithms designed to estimate the importance of input variables in feed-forward artificial neural network trained by back propagation algorithm (ANN) applications and to prune nonrelevant ones in a statistically reliable way. The analyzed algorithms performed similar variable estimations for simulated data sets, but differences were detected for real QSAR examples. Improvement of ANN prediction ability was shown after the pruning of redundant input variables. The statistical coefficients computed by ANNs for QSAR examples were better than those of multiple linear regression. Restrictions of the proposed algorithms and the potential use of ANNs are discussed.

Databases, Factual↗
PubMedDate not supplied

Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting.

The present study investigates an application of artificial neural networks (ANNs) for use in pharmaceutical fingerprinting. Several pruning algorithms were applied to decrease the dimension of the input parameter data set. A localized fingerprint region was identified within the original input parameter space from which a subset of input parameters was extracted leading to enhanced ANN performance. The present results confirm that ANNs can provide a fast, accurate, and consistent methodology applicable to pharmaceutical fingerprinting.

Algorithms↗