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Structural Classification (structural + classification)

Distribution by Scientific Domains

Chemistry	80%

Selected Abstracts

The use of structural species size classes in the description of the woody vegetation of a nature reserve

AFRICAN JOURNAL OF ECOLOGY, Issue 4 2004
L. R. Brown
Abstract The need for a scientifically based wildlife management plan and more knowledge on the vegetation ecology of the Borakalalo Nature Reserve prompted an ecological investigation of the Reserve. One of the aims was to develop a structural classification of the woody component using species size (SPIZE) classes. A further aim was to compare the various structural classes identified with the recognized floristically derived plant communities of the Reserve. The frequency, density, percentage crown cover and importance value for each woody species were calculated. A classification of the woody component was done using a TWINSPAN classification algorithm on this structural density data. Fifteen structural SPIZE classes were identified, described and compared with the described plant communities. The results of this study indicate that structural SPIZE classes could also be used to explain the spatial distribution of woody species within and between various plant communities. Résumé Le besoin de concevoir un plan d'aménagement scientifique, et un désir d'en savoir plus sur l'écologie de la végétation dans la Réserve Naturelle de Borakalalo, ont menéà une étude de cette Réserve. Un des buts principaux était d'établir une classification structurale de la partie boisée en fonction des catégories de taille des espèces (des catégories dites ,SPIZE', ou ,species size classes'). L'étude avait aussi comme but de comparer les diverse catégories structurelles identifiées avec les communautés végétales reconnues de la Réserve de dérivation floristique. La fréquence, la densité, le pourcentage de couverture de la cime et l'importance de chaque espèce boisée ont été calculés. Une classification de la partie boisée a été réalisée en se servant d'un algorithme dit ,TWINSPAN' pour évaluer les données sur la densité structurale. Quinze catégories SPIZE ont été identifiées, décrites et comparées avec les communautés végétales décrites. Les résultats de cette étude indiquent que les catégories SPIZE structurelles pourraient être utilisées davantage pour expliquer la distribution spatiale des espèces boisées entre et parmi les diverses communautés végétales. [source]

Using support vector machines for prediction of protein structural classes based on discrete wavelet transform

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 8 2009
Jian-Ding Qiu
Abstract The prediction of secondary structure is a fundamental and important component in the analytical study of protein structure and functions. How to improve the predictive accuracy of protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. In this study, a new method, in which the support vector machine combines with discrete wavelet transform, is developed to predict the protein structural classes. Its performance is assessed by cross-validation tests. The predicted results show that the proposed approach can remarkably improve the success rates, and might become a useful tool for predicting the other attributes of proteins as well. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009 [source]

Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 4 2006
Xuan Xiao
Abstract The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60], a novel approach for measuring the complexity of a protein sequence was introduced. The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife crossvalidation test that the overall success rate by the new approach was significantly higher than those by the others. It has not escaped our notice that the introduction of the complexity measure factor can also be used to improve the prediction quality for, among many other protein attributes, subcellular localization, enzyme family class, membrane protein type, and G-protein couple receptor type. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 478,482, 2006 [source]

Neuro-fuzzy structural classification of proteins for improved protein secondary structure prediction

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 8 2003
Joachim A. Hering
Abstract Fourier transform infrared (FTIR) spectroscopy is a very flexible technique for characterization of protein secondary structure. Measurements can be carried out rapidly in a number of different environments based on only small quantities of proteins. For this technique to become more widely used for protein secondary structure characterization, however, further developments in methods to accurately quantify protein secondary structure are necessary. Here we propose a structural classification of proteins (SCOP) class specialized neural networks architecture combining an adaptive neuro-fuzzy inference system (ANFIS) with SCOP class specialized backpropagation neural networks for improved protein secondary structure prediction. Our study shows that proteins can be accurately classified into two main classes "all alpha proteins" and "all beta proteins" merely based on the amide I band maximum position of their FTIR spectra. ANFIS is employed to perform the classification task to demonstrate the potential of this architecture with moderately complex problems. Based on studies using a reference set of 17 proteins and an evaluation set of 4 proteins, improved predictions were achieved compared to a conventional neural network approach, where structure specialized neural networks are trained based on protein spectra of both "all alpha" and "all beta" proteins. The standard errors of prediction (SEPs) in % structure were improved by 4.05% for helix structure, by 5.91% for sheet structure, by 2.68% for turn structure, and by 2.15% for bend structure. For other structure, an increase of SEP by 2.43% was observed. Those results were confirmed by a "leave-one-out" run with the combined set of 21 FTIR spectra of proteins. [source]