Biomedical subjects

Goran Srecnik

Publications and source records attributed to Goran Srecnik.

3 recordsLinked to original sources

PubMed2006

Novel approach to evolutionary neural network based descriptor selection and QSAR model development.

Capability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh's filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance.

Benzodiazepines↗

PubMed2005

Evaluation of novel sample identification approach based on chromatographic fingerprint set correlation homogeneity analysis.

Instead of usual rationale for chromatographic fingerprint based sample identification which relies upon visual inspection or principal component analysis of raw or aligned chromatograms novel nonparametric statistical measure of fingerprint set homogeneity is proposed. Randomization test is applied for significance analysis of fingerprint set homogeneity while average maximum crosscorrelation is used as a merit function. Chromatogram sets generated by random selection from standard and unknown sample chromatogram collections are compared with respect to merit function values with set of chromatograms that represents standard and/or unknown sample. In that instance fingerprint homogeneity significance is represented by the fraction of random chromatogram sets that have higher merit values than the standard and/or unknown sample sets. A set of peptide maps corresponding to different haemoglobin variants has been selected for evaluation of proposed test. This approach is compared to chromatogram alignment based on correlation optimized warping coupled with principal component or cluster analysis. Proposed method is simple i.e. straightforward sample identification procedure which reliability has been evaluated here. Impact of this approach on peptide mapping validation and system suitability analysis is discussed.

Chromatography, Liquid↗

PubMed2002

Optimization of artificial neural networks used for retention modelling in ion chromatography.

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.

Chromatography, Liquid↗