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High Predictive Ability (high + predictive_ability)

Distribution by Scientific Domains

Chemistry	50%

Selected Abstracts

Neural network modeling of physical properties of chemical compounds

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 1 2001
J. Kozio
Abstract Three different models relating structural descriptors to normal boiling points, melting points, and refractive indexes of organic compounds have been developed using artificial neural networks. A newly elaborated set of molecular descriptors was evaluated to determine their utility in quantitative structure,property relationship (QSPR) studies. Applying two data sets containing 190 amines and 393 amides, neural networks were trained to predict physical properties with close to experimental accuracy, using the conjugated gradient algorithm. Obtained results have shown a high predictive ability of learned neural networks models. The fit error for the predicted properties values compared to experimental data is relatively small. © 2001 John Wiley & Sons, Inc. Int J Quant Chem 84: 117,126, 2001 [source]

Serum eosinophil granule proteins predict asthma risk in allergic rhinitis

ALLERGY, Issue 5 2009
L. P. Nielsen
Background:, Allergic rhinitis is a common disease, in which some patients will deteriorate or develop asthma. It is important to characterize these patients, thereby offering the possibility for prevention. This study evaluated eosinophil parameters as potential indicators of deteriorating allergic airway disease. Methods:, The subjects of the study included all patients who suffered seasonal allergic rhinitis and had participated in a study 6 years earlier, in which blood eosinophils, serum eosinophil cationic protein (ECP) serum eosinophil peroxidase (EPO), nasal lavage ECP and nasal lavage EPO levels were measured. Patients in the present study were interviewed on occurrence of rhinitis symptoms during the last season, rhinitis outside season, asthma-like symptoms and asthma diagnosis, and were skin-prick tested for common aeroallergens. Eosinophil parameters from the study 6 years earlier were then tested for the ability to predict occurrence of new allergies, worsening of rhinitis and occurrence of asthma. Results:, Forty-four patients participated in the study. In four patients seasonal rhinitis symptoms had deteriorated, 10 had experienced perennial rhinitis symptoms, 14 reported asthma-like symptoms and seven had been diagnosed with asthma. Thirteen had developed additional sensitization. Patients developing asthma-like symptoms compared with patients with no such symptoms had significantly higher serum ECP (16.7 ,g/l vs 8.2 ,g/l; P , 0.01) and serum EPO (17.9 ,g/l vs 8.8 ,g/l; P , 0.05). Results were similar, considering patients diagnosed with asthma. Blood eosinophils and nasal lavage parameters were not related to development of asthma and asthma-like symptoms. No eosinophil parameter was related to deterioration of rhinitis or additional sensitization. Conclusion:, Serum ECP and EPO in patients with seasonal rhinitis demonstrated a high predictive ability for later development of asthma. [source]

Identification of Novel CDK2 Inhibitors by QSAR and Virtual Screening Procedures

MOLECULAR INFORMATICS, Issue 11-12 2008
Ajay Babu, Padavala
Abstract Quantitative Structure,Activity Relationship (QSAR) studies were carried out on a set of 46 imidazo[1,2-a]pyridines, imidazo[1,2-b]pyridazines and 2,4-bis anilino pyrimidines, and nitroso-6-aminopyrimidine and 2,6-diaminopyrimidine inhibitors of CDK2 (Cyclin-dependent Kinase2) using a multiple regression procedure. The activity contributions of these compounds were determined from regression equation and the validation procedures such as external set cross-validation r2, (R2cv,ext) and the regression of observed activities against predicted activities and vice versa for validation set were described to analyze the predictive ability of the QSAR model. An accurate and reliable QSAR model involving five descriptors was chosen based on the FIT Kubinyi function which defines the statistical quality of the model. The proposed model due to its high predictive ability was utilized to screen similar repertoire of compounds reported in the literature, and the biological activities are estimated. The screening study clearly demonstrated that the strategy presented shall be used as an alternative to the time-consuming experiments as the model tolerated a variety of structural modifications signifying its potential for drug design studies. [source]

Periglacial distribution modelling with a boosting method

PERMAFROST AND PERIGLACIAL PROCESSES, Issue 1 2009
Jan Hjort
Abstract We assessed the applicability of a boosting method in periglacial distribution modelling using empirically derived data on cryoturbation, sporadic permafrost and sorted solifluction from an area of 600,km2 in sub-Arctic Finland. The main aims were: (1) to compare the predictive ability of the generalised boosting method used with more common parametric techniques (generalised linear model) and machine-learning methods (artificial neural networks) and (2) to assess the tenability of the explanatory variables highlighted by the generalised boosting method. The results showed the robustness of the boosting method in predicting the distribution of periglacial phenomena in the sub-Arctic landscape. Furthermore, the environmental factors selected by the boosting method coincided well with the expected controls of the phenomena. The strengths of the generalised boosting method lie in its high predictive ability, flexibility in capturing complex process-environment relationships and realistic model outcomes. Copyright © 2008 John Wiley & Sons, Ltd. [source]